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CAMP proposes a hierarchical cache subsystem for multi-core mixed criticality processors, focusing on ensuring worst-case execution time (WCET) predictability in automotive applications. It incorporates criticality-aware locked L1 and L2 caches, reconfigurable at mode change intervals, along with criticality-aware last level cache partitioning. Evaluation using CACOSIM, Moola Multicore simulator, and CACTI simulation tools confirms the suitability of CAMP for keeping high-criticality jobs within timing budgets. A practical case study involving an automotive wake-up controller using the sniper v7.2 architecture simulator further validates its usability in real-world mixed criticality applications. CAMP presents a promising cache architecture for optimized multi-core mixed criticality systems.

Indirect Time-of-Flight (iToF) sensors measure the received signal's phase shift or time delay to calculate depth. In realistic conditions, however, recovering depth is challenging as reflections from secondary scattering areas or translucent objects may interfere with the direct reflection, resulting in inaccurate 3D estimates. We propose a new measurement concept including a single-shot on-chip multifrequency demodulation method with periodically-repeated ultrashort-pulsed illumination using a novel pixel array architecture to address a main limitation of conventional iToF, the Multi-Path Interference (MPI). Due to the careful hardware/software codesign, the proposed single-shot architecture provides close-to-optimal Fourier measurements to a spectral estimation algorithm that retrieves the unknown parameters of the interfering return paths in a closed form. Electrical simulations of the on-chip multifrequency demodulation circuit demonstrate the feasibility of distance retrieval in double and triple bounce conditions in a single shot with high accuracy. Furthermore, we propose a set of methods for processing the resulting sensor measurements that exploit valuable a priori information and structural constraints of the data and observe that they yield a substantial increase in accuracy

Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this paper, a novel vRAN reconfiguration problem is formulated to jointly reconfigure the functional splits of the base stations (BSs), locations of the virtualized central units (vCUs) and distributed units (vDUs), their resources, and the routing for each BS data flow. The objective is to minimize the long-term total network operation cost while adapting to the varying traffic demands and resource availability. In the first step, testbed measurements are performed to study the relationship between the traffic demands and computing resources, which reveals high variance and depends on the platform and its load. Consequently, finding the perfect model of the underlying system is non-trivial. Therefore, to solve the proposed problem, a deep reinforcement learning (RL)-based framework is proposed and developed using model-free RL approaches. Moreover, the problem consists of multiple BSs sharing the same resources, which results in a multi-dimensional discrete action space and leads to a combinatorial number of possible actions. To overcome this curse of dimensionality, action branching architecture, which is an action decomposition method with a shared decision module followed by neural network is combined with Dueling Double Deep Q-network (D3QN) algorithm. Simulations are carried out using an O-RAN compliant model and real traces of the testbed. Our numerical results show that the proposed framework successfully learns the optimal policy that adaptively selects the vRAN configurations, where its learning convergence can be further expedited through transfer learning even in different vRAN systems. It also offers significant cost savings by up to 59% of a static benchmark, 35% of Deep Deterministic Policy Gradient with discretization, and 76% of non-branching D3QN., Networked Systems

Reconfigurable intelligent surfaces (RISs) are considered a key enabler of highly energy-efficient 6G and beyond networks. This property arises from the absence of power amplifiers in the structure, in contrast to active nodes, such as small cells and relays. However, a certain amount of power is still required for RIS operation. To improve their energy efficiency further, we propose the notion of perpetual RISs, which secure the power needed to supply their functionalities through wireless energy harvesting (EH) of impinging transmitted electromagnetic (EM) signals. Toward this, we initially explain the rationale behind such RIS capability and proceed with a presentation of the main RIS controller architecture that can realize this vision under an in-band EH consideration. Furthermore, we present a typical EH architecture, followed by two harvesting protocols. Subsequently, we study the performance of the two protocols under a typical communications scenario. Finally, we elaborate on the main research challenges governing the realization of large-scale networks with perpetual RISs., This work was supported by the Luxembourg National Research Fund (FNR)–RISOTTI Project under Grant 14773976., Peer Reviewed, Postprint (published version)

Modern applications like Genomics and Machine Learning (ML) hold the potential to reshape our understanding of diseases’ genetic origins and guide machines in executing tasks and making predictions without our explicit programming. The successful, widespread integration of these modern applications can usher in advancements in di-agnostics, individualized medicine, and routine tasks such as language interpretation, image analysis, and object categorization. However, our traditional computing infrastructures fall short when accommodating the distinct characteristics of these new applications. Specifically, (1) these applications handle an immense and ever-expanding data working set, and (2) each succeeding version of these applications and their associated use cases necessitates quicker and more energy-efficient analysis of these vast data sets. This is because our traditional computing systems largely hinge on (1) the von-Neumann architecture, a design that distinctly positions processing entities (like CPUs and GPUs) away from storage components (like memories and flash drives), and (2) the CMOS-based technology. While attempting to meet the performance and energy demands of our modern applications, these fully CMOS-based systems based on von-Neumann architecture have increasingly struggled and hit inherent roadblocks, with data movement overhead being the predominant issue. To alleviate the data movement bottleneck, contemporary research revisits a concept historically known as Computation-In-Memory (CIM) or, alternatively, Processing-In-Memory (PIM). At its core, CIM emphasizes positioning computational capabilities close to, or within, the memory units storing the data. This placement might be within memory chips, in memory controllers, amid caches, or embedded in the logic layers of 3D-stacked memories. As a computational model, architectures leveraging CIM (referred to as CIM architectures) stand to tackle the issue of data movement overhead inherent, Computer Engineering

Computing-in-memory (CIM) architecture has become a possible solution to designing an energy-efficient artificial intelligent processor. Various CIM demonstrators indicated the computing efficiency of CIM macro and CIM-based processors. However, previous studies mainly focus on macro optimization and low CIM capacity without considering the weight update strategy of CIM architecture. The artificial intelligence (AI) processor with a CIM engine practically induces issues, including updating memory data and supporting different operators. For instance, AI-oriented applications usually contain various weight parameters. The weight stored in the CIM architecture should be reloaded for the considerable gap between the capacity of CIM and growing weight parameters. The computation efficiency of the CIM architecture is reduced by the weight updating and waiting. In addition, the natural parallelism of CIM leads to the mismatch of various convolution kernel sizes in different networks and layers, which reduces hardware utilization efficiency. In this work, we develop a CIM engine with a ping-pong computing strategy as an alternative to typical CIM macro and weight buffer, hiding the data update latency and improving the data reuse ratio. Based on the pingpong engine, we propose a flexible CIM architecture adapting to different sizes of neural networks, namely, intelligent pong computing-in memory (IPOCIM), with a fine-grained data flow mapping strategy. Based on the evaluation, IPOCIM can achieve a 1.27-6.27x performance and 2.34-5.30x energy efficiency improvement compared to the state-of-the-art works.

Bluetooth Low Energy (BLE) beacon network is one of the essential infrastructures for many IoT and smart city applications. However, the BLE beacon network usually suffers from poor reliability and high maintenance costs due to the short-lived battery lifetime. A few recent works tackled the challenge by adjusting the operating configuration subject to the nearby user occupancy in a reactive manner. However, previous works failed to adapt to dynamically changing user occupancy behaviors due to the lack of a prediction mechanism. Such shortcomings lead to a shorter lifetime and longer packet arrival delays. To overcome this limitation, a novel neural network architecture that makes accurate and timely user occupancy prediction is introduced. The proposed network learns partial correlation of the time-series data and attention score for robust prediction. The predictions are then utilized to adaptively change the operation configurations to maximize the lifetime and minimize the packet arrival delay. To the best of our knowledge, this is the first work to leverage time-series prediction to optimize the performance of a BLE beacon. The effectiveness of the proposed learning methods is verified by comprehensive simulations with real-life data. The results demonstrate that the proposed method can extend a beacon lifetime by 50% more than the existing reactive approach. Moreover, the packet arrival delays are also reduced by up to 40%. IEEE

With the popularity of video/audio streaming applications in recent years, the wide spread of Autonomous Sensory Meridian Response (ASMR) erotica content is becoming a serious issue in social networks. Due to the subtle nature of ASMR erotica and its relative rareness in real scenario, detecting ASMR erotica contents is a challenging task. In this paper, we propose a novel neural framework for ASMR erotica content moderation. The proposed framework consists of a pipeline of novel strategies to tackle challenges unique in ASMR Erotica Contents such as data scarcity and imbalanced data. Based on large-scale industrial data, the proposed framework demonstrates high moderation accuracy in quantitative analysis and significantly outperforming the existing counterparts.

Digital Twin of the Organization (DTO) is a relatively new concept that emerged to help managers have a full understanding of their organization and realize their objectives. Indeed, DTO enables connecting all the elements of an organization virtually by providing monitoring, optimization, prediction, and other capabilities through continuous simulations. Creating a flexible and evolvable DTO that covers and supports the organization's business strategies is a complex and time-consuming task that requires engineering best practices. In this context, this paper presents and evaluates the EA Blueprint Pattern, which serves as an architectural reference for the development of a DTO by allowing for mapping well-known Enterprise Architecture concepts into software components defining the DTO software architecture. The evaluation is carried on by showing how to use the pattern for creating the DTO for a given organization. Then, a thorough discussion is conducted to analyze how the developed DTO should evolve to deal with vertical and horizontal integration. The lessons learned highlight the strengths and weaknesses along with practical implications for organizations that are eager to develop their DTO according to the EA Blueprint Pattern.

Manufacturing is becoming increasingly complex as product life cycles shorten, and new disruptive technologies are introduced. The increased complexity in the manufacturing footprint also complicates industrial decision-making. Proposed improvements to alleviate bottlenecks do not guarantee effective problem resolution. Instead, improvement efforts can become misguided, targeting a bottleneck that affects a single production line rather than the entire site. An effective method for identifying production issues and predicting system performance is discrete-event simulation. When coupled with multi-objective optimization and multi-level modeling, production performance issues can be identified at both the site and workstation levels. However, optimization studies yield vast amounts of data, which can be challenging to extract useful knowledge from. To address this, we employ data-mining methods to assist decision-makers in extracting valuable insights from optimization data. This study presents an architecture for a decision support system that utilizes simulation-based optimization to continuously aid in industrial decision-making. Through a novel model generation method, simulation models are automatically generated and updated using logged data from the manufacturing shop floor and product lifecycle management systems. To reduce the computational complexity of the optimization, model simplification, varying replication numbers, surrogate modeling, and parallel computing in the cloud are also employed within this architecture. The results are presented to a decision-maker in an intelligent decision-support system, allowing for timely and relevant industrial decisions., CC BY-NC 4.0 DEED© 2024 The AuthorsCorrespondence Address: S. Lidberg; Högskolan i Skövde, Högskolevägen, Skövde, Box 408, Sweden; email: simon.lidberg@his.se

Digital Twin of the Organization (DTO) is a relatively new concept that emerged to help managers have a full understanding of their organization and realize their objectives. Indeed, DTO enables connecting all the elements of an organization virtually by providing monitoring, optimization, prediction, and other capabilities through continuous simulations. Creating a flexible and evolvable DTO that covers and supports the organization's business strategies is a complex and time-consuming task that requires engineering best practices. In this context, this paper presents and evaluates the EA Blueprint Pattern, which serves as an architectural reference for the development of a DTO by allowing for mapping well-known Enterprise Architecture concepts into software components defining the DTO software architecture. The evaluation is carried on by showing how to use the pattern for creating the DTO for a given organization. Then, a thorough discussion is conducted to analyze how the developed DTO should evolve to deal with vertical and horizontal integration. The lessons learned highlight the strengths and weaknesses along with practical implications for organizations that are eager to develop their DTO according to the EA Blueprint Pattern.

Manufacturing is becoming increasingly complex as product life cycles shorten, and new disruptive technologies are introduced. The increased complexity in the manufacturing footprint also complicates industrial decision-making. Proposed improvements to alleviate bottlenecks do not guarantee effective problem resolution. Instead, improvement efforts can become misguided, targeting a bottleneck that affects a single production line rather than the entire site. An effective method for identifying production issues and predicting system performance is discrete-event simulation. When coupled with multi-objective optimization and multi-level modeling, production performance issues can be identified at both the site and workstation levels. However, optimization studies yield vast amounts of data, which can be challenging to extract useful knowledge from. To address this, we employ data-mining methods to assist decision-makers in extracting valuable insights from optimization data. This study presents an architecture for a decision support system that utilizes simulation-based optimization to continuously aid in industrial decision-making. Through a novel model generation method, simulation models are automatically generated and updated using logged data from the manufacturing shop floor and product lifecycle management systems. To reduce the computational complexity of the optimization, model simplification, varying replication numbers, surrogate modeling, and parallel computing in the cloud are also employed within this architecture. The results are presented to a decision-maker in an intelligent decision-support system, allowing for timely and relevant industrial decisions., CC BY-NC 4.0 DEED© 2024 The AuthorsCorrespondence Address: S. Lidberg; Högskolan i Skövde, Högskolevägen, Skövde, Box 408, Sweden; email: simon.lidberg@his.se

The article discusses emerging non-von Neumann computer architectures and their integration in the computing continuum for supporting modern distributed applications, including artificial intelligence, big data, and scientific computing. It provides a detailed summary of available and emerging non-von Neumann architectures, which range from power-efficient single-board accelerators to quantum and neuromorphic computers. Furthermore, it explores their potential benefits for revolutionizing data processing and analysis in various societal, science, and industry fields. The article provides a detailed analysis of the most widely used class of distributed applications and discusses the difficulties in their execution over the computing continuum, including communication, interoperability, orchestration, and sustainability issues.

Low latency access to both data and instructions is paramount for processor performance. However, memory speed has been trailing behind the processor speed and is now a dominant bottleneck in execution. While both data and instruction misses cause performance losses, data misses can be overlapped with other useful work, but instruction misses stall the front-end of the processor leading to greater performance loss than data misses. Memory access characterization is important for designing memory hierarchies. While many works have characterised SPEC benchmark's memory behaviour, the results have been either tied to a specific micro-architecture or ignored the time-based behaviour of the benchmarks. In this thesis, we remove a majority of the micro-architectural features to characterize the intrinsic memory behaviour of the SPEC benchmarks and use this to understand how the workloads behave with various cache sizes and prefetching. In order to simplify the analysis of complex time-based results, we propose the use of MPKI Bins which divide the execution into distinct MPKI ranges. Using MPKI bins, we demonstrate that short memory-bound phases cause a significant percentage of the overall cache misses. For instructions, the growing instruction footprints of server workloads are causing significant performance losses due to front-end stalls that cannot be overlapped or hidden by out-of-order execution. The second part of this thesis develops a technique to enable dedicated instruction prefetchers without the area cost of separate metadata storage structures. We propose to re-purpose the branch target buffer (BTB) to store prefetcher metadata based on the insight that benchmarks that require a dedicated instruction prefetcher can tolerate increased BTB misses. Going further, we propose L2 instruction bypassing based on the insight that decreased L2 data misses deliver more benefit then the slight instruction latency reduction of having instructions in the L2. We show tha

Manufacturing is becoming increasingly complex as product life cycles shorten, and new disruptive technologies are introduced. The increased complexity in the manufacturing footprint also complicates industrial decision-making. Proposed improvements to alleviate bottlenecks do not guarantee effective problem resolution. Instead, improvement efforts can become misguided, targeting a bottleneck that affects a single production line rather than the entire site. An effective method for identifying production issues and predicting system performance is discrete-event simulation. When coupled with multi-objective optimization and multi-level modeling, production performance issues can be identified at both the site and workstation levels. However, optimization studies yield vast amounts of data, which can be challenging to extract useful knowledge from. To address this, we employ data-mining methods to assist decision-makers in extracting valuable insights from optimization data. This study presents an architecture for a decision support system that utilizes simulation-based optimization to continuously aid in industrial decision-making. Through a novel model generation method, simulation models are automatically generated and updated using logged data from the manufacturing shop floor and product lifecycle management systems. To reduce the computational complexity of the optimization, model simplification, varying replication numbers, surrogate modeling, and parallel computing in the cloud are also employed within this architecture. The results are presented to a decision-maker in an intelligent decision-support system, allowing for timely and relevant industrial decisions., CC BY-NC 4.0 DEED© 2024 The AuthorsCorrespondence Address: S. Lidberg; Högskolan i Skövde, Högskolevägen, Skövde, Box 408, Sweden; email: simon.lidberg@his.se

Manufacturing is becoming increasingly complex as product life cycles shorten, and new disruptive technologies are introduced. The increased complexity in the manufacturing footprint also complicates industrial decision-making. Proposed improvements to alleviate bottlenecks do not guarantee effective problem resolution. Instead, improvement efforts can become misguided, targeting a bottleneck that affects a single production line rather than the entire site. An effective method for identifying production issues and predicting system performance is discrete-event simulation. When coupled with multi-objective optimization and multi-level modeling, production performance issues can be identified at both the site and workstation levels. However, optimization studies yield vast amounts of data, which can be challenging to extract useful knowledge from. To address this, we employ data-mining methods to assist decision-makers in extracting valuable insights from optimization data. This study presents an architecture for a decision support system that utilizes simulation-based optimization to continuously aid in industrial decision-making. Through a novel model generation method, simulation models are automatically generated and updated using logged data from the manufacturing shop floor and product lifecycle management systems. To reduce the computational complexity of the optimization, model simplification, varying replication numbers, surrogate modeling, and parallel computing in the cloud are also employed within this architecture. The results are presented to a decision-maker in an intelligent decision-support system, allowing for timely and relevant industrial decisions., CC BY-NC 4.0 DEED© 2024 The AuthorsCorrespondence Address: S. Lidberg; Högskolan i Skövde, Högskolevägen, Skövde, Box 408, Sweden; email: simon.lidberg@his.se

The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs. However, manually crafting machine learning models remains a complex and time-consuming process because it requires extensive human expertise and tremendous engineering efforts to carefully extract features and design model architectures. In this work, we leverage automated machine learning techniques to automate the machine learning (ML) model development for routability prediction, a well-established technique that can help to guide cell placement toward routable solutions. We present an automated feature selection method to identify suitable features for model inputs.We develop a neural architecture search method to search for high-quality neural architectures without human interference. Our search method supports various operations and highly flexible connections, leading to architectures significantly different from all previous human-crafted models. Our experimental results demonstrate that our automatically generated models clearly outperform multiple representative manually crafted solutions with a superior 9.9% improvement. Moreover, compared with human-crafted models, which easily take weeks or months to develop, our efficient AutoML framework completes the whole model development process with only 1 day. IEEE

Low latency access to both data and instructions is paramount for processor performance. However, memory speed has been trailing behind the processor speed and is now a dominant bottleneck in execution. While both data and instruction misses cause performance losses, data misses can be overlapped with other useful work, but instruction misses stall the front-end of the processor leading to greater performance loss than data misses. Memory access characterization is important for designing memory hierarchies. While many works have characterised SPEC benchmark's memory behaviour, the results have been either tied to a specific micro-architecture or ignored the time-based behaviour of the benchmarks. In this thesis, we remove a majority of the micro-architectural features to characterize the intrinsic memory behaviour of the SPEC benchmarks and use this to understand how the workloads behave with various cache sizes and prefetching. In order to simplify the analysis of complex time-based results, we propose the use of MPKI Bins which divide the execution into distinct MPKI ranges. Using MPKI bins, we demonstrate that short memory-bound phases cause a significant percentage of the overall cache misses. For instructions, the growing instruction footprints of server workloads are causing significant performance losses due to front-end stalls that cannot be overlapped or hidden by out-of-order execution. The second part of this thesis develops a technique to enable dedicated instruction prefetchers without the area cost of separate metadata storage structures. We propose to re-purpose the branch target buffer (BTB) to store prefetcher metadata based on the insight that benchmarks that require a dedicated instruction prefetcher can tolerate increased BTB misses. Going further, we propose L2 instruction bypassing based on the insight that decreased L2 data misses deliver more benefit then the slight instruction latency reduction of having instructions in the L2. We show tha

The pervasive deployment of Deep Learning models has recently prompted apprehensions regarding their ecological footprint, owing to the exorbitant levels of energy consumption necessitated by the training and inference processes. The term "Red AI" is employed to denote artificial intelligence (AI) models that undergo training using resource-intensive methodologies on very large datasets. This practice can engender substantial energy usage and emissions of carbon, thereby opposing "Green AI. " The latter concept alludes to AI models designed for similar efficiency and reduced environmental impact. This objective is realized through the utilization of smaller datasets, less computationally intensive training techniques, or sustainable energy resources. While Red AI prioritizes accuracy and performance, Green AI emphasizes efficiency and sustainability. Given that both paradigms exhibit advantages and limitations, the debates around the topics have burgeoned in the scientific arena, delving into novel algorithms, hardware innovations, and improved data utilization techniques aimed at mitigating the ecological consequences of intricate applications such as GPT and BERT. Nevertheless, due to the relative novelty of this debate, not much effort has been dedicated yet to contextualizing the essence of Red AI and the prospects of Green AI in a coherent framework. Within this context, the present work contributes by meticulously delineating both domains through a multifaceted analysis of their causes and ramifications, described from the points of computer architectures, data structures, and algorithms. Additionally, the study reviews notable instances of study cases based on complex Red AI models. The primary contribution of this article encompasses a comprehensive survey of Red and Green AI, stemming from a selection of the literature performed by the authors, subsequently organized into distinct clusters. These clusters encompass i) articles that qualitatively or quantita

La vida en las ciudades y pueblos a menudo puede ser compleja. Tomar decisiones es habitualmente una tarea que se deja en manos de unas pocas personas. ¿Qué tal si lo cambiamos? Gobierno Abierto App sirve para democratizar las ciudades con la intención que los ciudadanos participen de forma directa en la planificación de su ciudad. La tarea es sencilla: cada ciudadano de forma individual podrá enviar propuestas, adjuntando una serie de datos (localización, imagen, algún plano si es necesario...) con tal de que el resto de personas de la ciudad puedan votar la propuesta. Si la propuesta es elegida, la administración deberá tener la voluntad de implementarla. Una de las características más importantes de Gobierno Abierto App es la utilización de encriptación homomórfica para la suma de los votos, de manera que los votos son anónimos para que nadie pueda saber qué ha votado cada persona individualmente, solo el conjunto. Gobierno Abierto App utiliza otras tecnologías y lenguajes de programación actuales como Golang, para el desarrollo del backend, y Flutter para el desarrollo del frontend, así como otras tecnologías que permiten dotar a la aplicación de una experiencia de usuario fluida, segura y accesible. Con la combinación de estas tecnologías, Gobierno Abierto App se convierte en una herramienta poderosa para la participación ciudadana, permitiendo a cada individuo tener un impacto directo en el desarrollo de su ciudad. ¡Hacer de nuestros municipios mejores lugares para vivir nunca ha sido tan fácil!

The rapid expansion of computational workloads in contemporary computing necessitates continuous advancements in computational systems. The proliferation of many-core architectures, with a doubling core count every two months, poses challenges in inter-core communication and data mobility. These challenges, driven by limitations in bandwidth and physical interconnections, impede the scalability of supercomputing systems. Research endeavors have been exploring strategies to optimize inter-core communications, including wireless integration and innovative broadcast mechanisms, to enhance the performance of high-performance computing (HPC). Many-core systems play a pivotal role in HPC, enabling concurrent task execution and unprecedented parallelism. This work seeks to improve HPC performance and scalability, focusing on inter-core communication in chiplet-based architectures. Traditional wired links in multi-chiplet systems face scalability and performance limitations, including intricate wiring, hardware failure risks, latency, and bandwidth challenges. The advent of wireless Network-on-Chip (NoC) technology offers an innovative solution, leveraging wireless communication to overcome these limitations. Wireless NoC provides benefits such as reduced complexity, flexible chip design, dynamic reconfiguration, and mitigating latency issues. However, the adoption of wireless NoC introduces unique challenges like interference, security, and power management. This thesis conducts a comprehensive exploration of wireless NoC's potential and challenges, aiming to advance high-performance computing and set the stage for future advancements. The research further introduces a hybrid approach in chiplet-based architectures for MPI collectives, where wired and wireless links are strategically combined. This approach optimizes message distribution, resulting in a substantial performance improvement. The findings emphasize an improved system speedup. In summary, this work underscores

Hoy en día existen multitud de lenguajes de programación para distintos propósitos. Uno de ellos es el desarrollo de aplicaciones y servicios web. La diversidad de opciones disponibles en el mercado actual presenta un desafío y una oportunidad para los desarrolladores, ya que deben sopesar factores cruciales como la facilidad de desarrollo, la eficiencia y el rendimiento. Este proyecto se embarca en una comparación y evaluación de diversos lenguajes de programación, con el objetivo de proporcionar una visión detallada y completa. En un escenario donde la evolución es constante y donde la demanda de aplicaciones y servicios web robustas y eficientes, comprender las fortalezas y debilidades de cada lenguaje se vuelve cada vez más esencial. Para poder analizar los lenguajes, se desarrollará una serie de aplicaciones en cuatro de los lenguajes estudiados a lo largo del proyecto y sobre el cual se realizará una serie de pruebas para evaluar su rendimiento y eficiencia de desarrollo. Se realizará un análisis de los distintos lenguajes de programación que se encuentran en el mercado actual para poder determinar cuáles son los más adecuados, todo ello desde el punto de vista del desarrollo web del lado del servidor (backend). Las principales características por comparar son el tipo de lenguaje, el rendimiento, la facilidad de despliegue, utilización por parte de los desarrolladores del sector TI y características de seguridad nativas., Today there are many programming languages for different purposes. One of them is the development of web applications and services. The diversity of options available in today's market presents a challenge and an opportunity for developers as they must weigh crucial factors such as ease of development, efficiency and performance. This project embarks on a comparison and evaluation of various programming languages, with the aim of providing a detailed and complete overview. In a scenario where evolution is constant and where the demand for robust and efficient web applications and services, understanding the strengths and weaknesses of each language becomes increasingly essential. In order to analyze the languages, a series of applications will be developed in four of the languages studied throughout the project and on which a series of tests will be carried out to evaluate their performance and development efficiency. An analysis of the different programming languages found in the current market will be carried out to determine which ones are the most suitable, all from the point of view of server-side (backend) web development. The main characteristics to compare are the type of language, performance, ease of deployment, use by developers in the IT sector and native security features.

In order to allow every user to extract aggregated computational power from idle PCs in the Internet, we propose a distributed architecture to achieve a market based resource sharing among users. The advantages of our proposed architecture are the following: (i) aggregated resources can be bought by one order; (ii) resource prices are decided based on market principles; and (iii) the load is balanced among multiple server nodes to make the architecture scalable w.r.t. the number of users. Through simulations, we have confirmed that the proposed method can mitigate the load at each server node to a great extent., CCGrid2005 : IEEE International Symposium on Cluster Computing and the Grid , May 9-12, 2005 , Cardiff, UK

In this paper, the authors propose a technique to flexibly implement genetic algorithms (GAs) for various problems on FPGAs. For the purpose, the authors propose a common architecture for GA. The proposed architecture allows designers to easily implement a GA as a hardware circuit consisting of parallel pipelines which execute GA operations. The proposed architecture is scalable to increase the number of parallel pipelines. The architecture is applicable to various problems and allows designers to estimate the size of resulting circuits. The authors give a model for predicting the size of resulting circuits from given parameters. Based on the proposed method, the authors have implemented a tool to facilitate GA circuit design and development. Through experiments using knapsack problem and traveling salesman problem (TSP), the authors show that the FPGA circuits synthesized based on the proposed method run much faster and consume much lower power than software implementation on a PC and the model can predict the size of the resulting circuit accurately enough., FCCM 2006 : 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines , Apr 24-26, 2006 , Napa, CA, USA

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Hyperdimensional computing (HDC) is an emerging computing paradigm that represents, manipulates, and communicates data using long random vectors known as hypervectors. Among different hardware platforms capable of executing HDC algorithms, in-memory computing (IMC) has shown promise as it is very efficient in performing matrix-vector multiplications, which are common in the HDC algebra. Although HDC architectures based on IMC already exist, how to scale them remains a key challenge due to collective communication patterns that these architectures required and that traditional chip-scale networks were not designed for. To cope with this difficulty, we propose a scale-out HDC architecture called WHYPE, which uses wireless in-package communication technology to interconnect a large number of physically distributed IMC cores that either encode hypervectors or perform multiple similarity searches in parallel. In this context, the key enabler of WHYPE is the opportunistic use of the wireless network as a medium for over-the-air computation. WHYPE implements an optimized source coding that allows receivers to calculate the bit-wise majority of multiple hypervectors (a useful operation in HDC) being transmitted concurrently over the wireless channel. By doing so, we achieve a joint broadcast distribution and computation with a performance and efficiency unattainable with wired interconnects, which in turn enables massive parallelization of the architecture. Through evaluations at the on-chip network and complete architecture levels, we demonstrate that WHYPE can bundle and distribute hypervectors faster and more efficiently than a hypothetical wired implementation, and that it scales well to tens of receivers. We show that the average error rate of the majority computation is low, such that it has negligible impact on the accuracy of HDC classification tasks., Postprint (author's final draft)

The use of artificial intelligence (AI) in sensor analytics is entering a new era based on the use of ubiquitous embedded connected devices. This transformation requires the adoption of design techniques that reconcile accurate results with sustainable system architectures. As such, improving the efficiency of AI hardware engines as well as backward compatibility must be considered. In this paper, we present the Hybrid-Float6 (HF6) quantization and its dedicated hardware design. We propose an optimized multiply-accumulate (MAC) hardware by reducing the mantissa multiplication to a multiplexor-adder operation. We exploit the intrinsic error tolerance of neural networks to further reduce the hardware design with approximation. To preserve model accuracy, we present a quantization-aware training (QAT) method, which in some cases improves accuracy. We demonstrate this concept in 2D convolution layers. We present a lightweight tensor processor (TP) implementing a pipelined vector dot-product. For compatibility and portability, the 6-bit floating-point (FP) is wrapped in the standard FP format, which is automatically extracted by the proposed hardware. The hardware/software architecture is compatible with TensorFlow (TF) Lite. We evaluate the applicability of our approach with a CNN-regression model for anomaly localization in a structural health monitoring (SHM) application based on acoustic emission (AE). The embedded hardware/software framework is demonstrated on XC7Z007S as the smallest Zynq-7000 SoC. The proposed implementation achieves a peak power efficiency and run-time acceleration of 5.7 GFLOPS/s/W and 48.3x, respectively., QC 20230227

With DRAM latencies increasing relative to CPU speeds, the performance of caches has become more important. This has led to increasingly sophisticated replacement policies that require complex calculations to update their replacement metadata, which often require multiple cycles. To minimize the negative impact of these metadata updates, architects have focused on policies that incur as little update latency as possible through a combination of reducing the policies’ precision and using parallel hardware. In this work we investigate whether these tradeoffs to reduce cache metadata update latency are needed. Specifically, we look at the performance and energy impact of increasing the latency of cache replacement policy updates. We find that even dramatic increases in replacement policy update latency have very limited effect. This indicates that designers have far more freedom to increase policy complexity and latency than previously assumed.

CNNs are difficult to achieve inter-layer parallelism because of the data dependence between layers. In the paper, we propose Symbolic-Execution CNN (SE-CNN), which breaks data dependence between CNN layers via value prediction. Our insight is that in post-trained CNNs, only a subset (less than 10%) of neurons are activated (producing non-zero values) to identify patterns in inputs while most of the other neurons remain silent (producing zeros). This is because given an input image there are only a limited number of features presented. Thus, within the CNN, the neurons that are sensitive to the given features are more exercised than the others. Based on this insight, SE-CNN works in two successive phases: A parallel computation phase and a serial correction phase. In the parallel computation phase, each CNN layer starts computation simultaneously based on predicted inputs: we predict most of the neurons having zeros as inputs. For non-zero input neurons we predict their inputs for the next input image the same as the previous ones. In the serial correction phase, each layer compares the predicted inputs with the real ones to correct its computation results if necessary. If a neuron has predicted correctly its input during the parallel phase, thus the corresponding neuron passes its serial phase. Otherwise the neuron will amend its prediction with a light-weight result amendment mechanism based on the real inputs. We imple-ment SE-CNN on top of the streaming processor of a state-of-the-art general purpose GPU (GPGPU) architecture, adding marginal hardware overheads in area and power consumption. We also provide application programming interfaces (APIs) so that CNNs that have already been implemented can directly enjoy the benefits of our technique. We utilize GPGPU-sim as our experimental platform, benchmarked with 9 well-accepted CNNs from recent years' ILSVRC contests. Experimental results show that compared to other three state-of-the-art GPU based CNN acceleratio

Speculative side-channel attacks have forced computer architects to rethink speculative execution. Effectively preventing microarchitectural state from leaking sensitive information will be a key requirement in future processor design. An important limitation of many secure speculation schemes is a reduction in the available memory parallelism, as unsafe loads (depending on the particular scheme) are blocked, as they might potentially leak information. Our contribution is to show that it is possible to recover some of this lost memory parallelism, by safely predicting the addresses of these loads in a threat-model transparent way, i.e., without worsening the security guarantees of the underlying secure scheme. To demonstrate the generality of the approach, we apply it to three different secure speculation schemes: Non-speculative Data Access (NDA), Speculative Taint Tracking (STT), and Delay-on-Miss (DoM). An address predictor is trained on non-speculative data, and can afterwards predict the addresses of unsafe slow-to-issue loads, preloading the target registers with speculative values, that can be released faster on correct predictions than starting the entire load process. This new perspective on speculative execution encompasses all loads, and gives speedups, separately from prefetching. We call the address-predicted counterparts of loads Doppelganger Loads. They give notable performance improvements for the three secure speculation schemes we evaluate, NDA, STT, and DoM. The Doppelganger Loads reduce the geometric mean slowdown by 42%, 48%, and 30% respectively, as compared to an unsafe baseline, for a wide variety of SPEC2006 and SPEC2017 benchmarks. Furthermore, Doppelganger Loads can be efficiently implemented with only minor core modifications, reusing existing resources such as a stride prefetcher, and most importantly, requiring no changes to the memory hierarchy outside the core.

The article discusses the emerging non-von Neumann computer architectures and their integration in the computing continuum for supporting modern distributed applications, including artificial intelligence, big data, and scientific computing. It provides a detailed summary of the available and emerging non-von Neumann architectures, which range from power-efficient single-board accelerators to quantum and neuromorphic computers. Furthermore, it explores their potential benefits for revolutionizing data processing and analysis in various societal, science, and industry fields. The paper provides a detailed analysis of the most widely used class of distributed applications and discusses the difficulties in their execution over the computing continuum, including communication, interoperability, orchestration, and sustainability issues.

This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [321, 326] is an influential HDC/VSA model that is well known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field.Part I of this survey [222] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain; however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners., Funding details: Air Force Office of Scientific Research, AFOSR, FA9550-19-1-0241; Funding details: Intel Corporation; Funding details: H2020 Marie Skłodowska-Curie Actions, MSCA, 839179; Funding details: Stiftelsen för Strategisk Forskning, SSF, UKR22-0024; Funding details: National Academy of Sciences of Ukraine, NASU, 0117U002286, 0120U000122, 0121U000016, 0122U002151; Funding details: Ministry of Education and Science of Ukraine, MESU, 0121U000228, 0122U000818; Funding text 1: The work of DK was supported by the European Union’s Horizon 2020 Programme under the Marie Skłodowska-Curie Individual Fellowship Grant (839179). The work of DK was also supported in part by AFOSR FA9550-19-1-0241 and Intel’s THWAI program. The work of DAR was supported in part by the National Academy of Sciences of Ukraine (grant no. 0120U000122, 0121U000016, 0122U002151, and 0117U002286), the Ministry of Education and Science of Ukraine (grant no. 0121U000228 and 0122U000818), and the Swedish Foundation for Strategic Research (SSF, grant no. UKR22-0024).; Funding text 2: The work of DK was supported by the European Union’s Horizon 2020 Programme under the Marie SkÅ odowska-Curie Individual Fellowship Grant (839179). The work of DK was also supported in part by AFOSR FA9550-19-1-0241 and Intel’s THWAI program. The work of DAR was supported in part by the National Academy of Sciences of Ukraine (grant no. 0120U000122, 0121U000016, 0122U002151, and 0117U002286), the Ministry of Education and Science of Ukraine (grant no. 0121U000228 and 0122U000818), and the Swedish Foundation for Strategic Research (SSF, grant no. UKR22-0024).

Quantum computing is a promising field for continuing to develop new computing capabilities, both in its own right and for continued gains as Moore's Law growth ends.Trapped ion quantum computing is a leading technology in the field of quantum computing, as it combines the important characteristics of high fidelity operations, individual addressing, and long coherence times. However, quantum computers are still in their infancy; the first quantum computers to have more than a handful of quantum bits (qubits) are less than a decade old. As research groups push the boundaries of the number of qubits in a system, they are consistently running into engineering obstacles preventing them from achieving their goals. There is effectively a knowledge gap between the physicists who have the capability to push the field of quantum computing forward, and the engineers who can design the large-scale & reliable systems that enable pushing those envelopes. This thesis is an attempt to bridge that gap by framing trapped ion quantum computing in a manner accessible to engineers, as well as improving on the state-of-the-art in quantum computer digital and RF control systems. We also consider some of the practical and theoretical engineering challenges that arise when developing a leading-edge trapped ion quantum computer capable of demonstrating error-corrected logical qubits, using trapped Ytterbium-171 qubits.There are many fundamental quantum operations that quantum information theory assumes, yet which are quite complicated to implement in reality. First, we address the time cost of rearranging a chain of ions after a scrambling collision with background gases. Then we consider a gate waveform generator that reduces programming time while supporting conditional quantum gates. Next, we discuss the development of a digital control system custom-designed for quantum computing and quantum networking applications. Finally, we demonstrate experimental results of the waveform generator

In recent years, there has been a growing demand for vector processors due to their increasing application in deep-learning applications. On the other hand, with the strong need for energy efficiency and high performance, heterogeneous architecture plays an important role and becomes increasingly complex. However, the way of connecting the memory hierarchy to the vector processor in SOC (System-on-Chip) is critical to the system’s performance [8]. This work presents tile design which is based on OpenPiton and BYOC [4] [3]. Tile consists of a 64-bit, single-issue, in-order RISC-V core Ariane [14], along with a 64-bit vector processor ARA [7] [13] which implemented RISC-V V extension version 1.0. This work makes the following contributions. First, it involves the design and implementation of an adapter (bridge) that converts memory request from AMBA AXI to OpenPiton NoC. This adapter enables ARA memory access functionality and facilitates the integration of future accelerators into OpenPiton. Secondly, a tile design is presented, which includes ARA, a RISC-V vector processor, Ariane (a RISC-V core), L1.5 cache, L2 cache, and the implemented bridge. The performance of the tile is evaluated using different versions of bridges connected to the last-level cache (LLC) or off-chip memory. The analysis indicates that a wide data width bridge does not necessarily improve performance significantly. Several factors, such as NoC traffic confliction or unused data fetch, can narrow the performance gap between small and large width bridges. Furthermore, the experiments demonstrate that memory exhibits advantages when dealing with large data widths, and memory saturation also occurs during LLC access. Finally, the thesis proposes the implementation of MSHR (Miss Status Handling Register) and extends this design to manycore architectures to enhance performance.

Hardware platforms, such as microprocessors and Trusted Execution Environments (TEEs), aim to provide strong memory isolation properties. However, in recent years, this has been shown not to be the case through hardware attacks such as the class of transient execution attacks. These attacks affect programs executing on widely-used microprocessor designs in our present-day devices. Although mitigations have been proposed, many have not been adopted and lack formal guarantees. As a result, security-critical applications have been conservative in using hardware platforms without some form of cryptographic approach for secure computation, despite the additional computational overhead. One approach to ensure safety for this class of attacks is to use formal methods to prove information flow properties. Yet, there is limited work in verifying attacks on hardware platforms that are heterogeneous in nature, namely those that contain hardware and software in the trusted computing base.This thesis defines a notion of secure information flow for hardware platforms and proposes methods to formally verify non-interference-based properties efficiently using abstractions and composition. To accomplish the former, we formalize the trace property-dependent observational determinism property for capturing a new class of non-interference properties. This property is motivated by verifying transient execution attacks and the need for secure speculation. To enable efficient verification on hardware platforms, we introduce an efficient proof system, SymboTaint, and the formalism of information flow state machines to reason about secure information flow compositionally. Finally, we explore a complementary method to enforce secure information flow for general programs by relaxing the programming model of a family of TEE designs and by formally verifying them. This direction builds on top of existing abstractions of TEEs to provide memory isolation guarantees with an efficient memory-sharin

Inference carried out on pretrained deep neural networks (DNNs) is particularly effective as it does not require retraining and entails no loss in accuracy. Unfortunately, resource-constrained devices such as those in the Internet of Things may need to offload the related computation to more powerful servers, particularly, at the network edge. However, edge servers have limited resources compared to those in the cloud; therefore, inference offloading generally requires dividing the original DNN into different pieces that are then assigned to multiple edge servers. Related approaches in the state-of-the-art either make strong assumptions on the system model or fail to provide strict performance guarantees. This article specifically addresses these limitations by applying distributed assignment to DNN inference at the edge. In particular, it devises a detailed model of DNN-based inference, suitable for realistic scenarios involving edge computing. Optimal inference offloading with load balancing is also defined as a multiple assignment problem that maximizes proportional fairness. Moreover, a distributed algorithm for DNN inference offloading is introduced to solve such a problem in polynomial time with strong optimality guarantees. Finally, extensive simulations employing different data sets and DNN architectures establish that the proposed solution significantly improves upon the state-of-the-art in terms of inference time (1.14 to 2.62 times faster), load balance (with Jain's fairness index of 0.9), and convergence (one order of magnitude less iterations)., QC 20230524

The article discusses the emerging non-von Neumann computer architectures and their integration in the computing continuum for supporting modern distributed applications, including artificial intelligence, big data, and scientific computing. It provides a detailed summary of the available and emerging non-von Neumann architectures, which range from power-efficient single-board accelerators to quantum and neuromorphic computers. Furthermore, it explores their potential benefits for revolutionizing data processing and analysis in various societal, science, and industry fields. The paper provides a detailed analysis of the most widely used class of distributed applications and discusses the difficulties in their execution over the computing continuum, including communication, interoperability, orchestration, and sustainability issues.

The article discusses the emerging non-von Neumann computer architectures and their integration in the computing continuum for supporting modern distributed applications, including artificial intelligence, big data, and scientific computing. It provides a detailed summary of the available and emerging non-von Neumann architectures, which range from power-efficient single-board accelerators to quantum and neuromorphic computers. Furthermore, it explores their potential benefits for revolutionizing data processing and analysis in various societal, science, and industry fields. The paper provides a detailed analysis of the most widely used class of distributed applications and discusses the difficulties in their execution over the computing continuum, including communication, interoperability, orchestration, and sustainability issues.

This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [321, 326] is an influential HDC/VSA model that is well known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field.Part I of this survey [222] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain; however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners., Funding details: Air Force Office of Scientific Research, AFOSR, FA9550-19-1-0241; Funding details: Intel Corporation; Funding details: H2020 Marie Skłodowska-Curie Actions, MSCA, 839179; Funding details: Stiftelsen för Strategisk Forskning, SSF, UKR22-0024; Funding details: National Academy of Sciences of Ukraine, NASU, 0117U002286, 0120U000122, 0121U000016, 0122U002151; Funding details: Ministry of Education and Science of Ukraine, MESU, 0121U000228, 0122U000818; Funding text 1: The work of DK was supported by the European Union’s Horizon 2020 Programme under the Marie Skłodowska-Curie Individual Fellowship Grant (839179). The work of DK was also supported in part by AFOSR FA9550-19-1-0241 and Intel’s THWAI program. The work of DAR was supported in part by the National Academy of Sciences of Ukraine (grant no. 0120U000122, 0121U000016, 0122U002151, and 0117U002286), the Ministry of Education and Science of Ukraine (grant no. 0121U000228 and 0122U000818), and the Swedish Foundation for Strategic Research (SSF, grant no. UKR22-0024).; Funding text 2: The work of DK was supported by the European Union’s Horizon 2020 Programme under the Marie SkÅ odowska-Curie Individual Fellowship Grant (839179). The work of DK was also supported in part by AFOSR FA9550-19-1-0241 and Intel’s THWAI program. The work of DAR was supported in part by the National Academy of Sciences of Ukraine (grant no. 0120U000122, 0121U000016, 0122U002151, and 0117U002286), the Ministry of Education and Science of Ukraine (grant no. 0121U000228 and 0122U000818), and the Swedish Foundation for Strategic Research (SSF, grant no. UKR22-0024).

GPGPU architectures have become the dominant platform for massively parallel workloads, delivering high performance and energy efficiency for popular applications such as machine learning, computer vision or self-driving cars. However, irregular applications, such as graph processing, fail to fully exploit GPGPU resources due to their divergent memory accesses that saturate the memory hierarchy. To reduce the pressure on the memory subsystem for divergent memory-intensive applications, programmers must take into account SIMT execution model and memory coalescing in GPGPUs, devoting significant efforts in complex optimization techniques. Despite these efforts, we show that irregular graph processing still suffers from low GPGPU performance. We observe that in many irregular applications the mapping of data to threads can be safely changed. In other words, it is possible to relax the strict relationship between thread and data processed to reduce memory divergence. Based on this observation, we propose the Irregular accesses Reorder Unit (IRU), a novel hardware extension tightly integrated in the GPGPU pipeline. The IRU reorders data processed by the threads on irregular accesses to improve memory coalescing, i.e., it tries to assign data elements to threads as to produce coalesced accesses in SIMT groups. Furthermore, the IRU is capable of filtering and merging duplicated accesses, significantly reducing the workload. Programmers can easily utilize the IRU with a simple API, or let the compiler issue instructions from our extended ISA. We evaluate our proposal for state-of-the-art graph-based algorithms and a wide selection of applications. Results show that the IRU achieves a memory coalescing improvement of 1.32x and a 46% reduction in the overall traffic in the memory hierarchy, which results in 1.33x speedup and 13% energy savings on average, while incurring in a small 5.6% area overhead., This work has been supported by the CoCoUnit ERC Advanced Grant of the EU’s Horizon 2020 program (grant No 833057), the Spanish State Research Agency (MCIN/AEI) under grant PID2020-113172RB-I00 and the ICREA Academia program., Peer Reviewed, Postprint (published version)

Software memory compression has been a common practice among operating systems. Since then, prior works have explored hardware memory compression to reduce the load on the CPU by offloading memory compression to hardware. However, prior works on hardware memory compression cannot provide critical isolation in multi-tenant systems like cloud servers. Our evaluation of prior work (TMCC) shows that a tenant can be slowed down by more than 12x due to the lack of isolation. This work, Compressed Memory Management Unit (CMMU), prototypes hardware compression for multi-tenant systems. CMMU provides critical isolation for multi-tenant systems.First, CMMU allows OS to control individual tenants' usage of physical memory. Second, CMMU compresses a tenant's memory to an OS-specified physical usage target. Finally, CMMU notifies the OS to start swapping the memory to the storage if it fails to compress the memory to the target. We prototype CMMU with a real compression module on an FPGA board. CMMU runs with a Linux kernel modified to support CMMU. The prototype virtually expands the memory capacity to 4X. CMMU stably supports the modified Linux kernel with multiple tenants and applications. While achieving this, CMMU only requires several extra cycles of overhead besides the essential data structure accesses. ASIC synthesis results show CMMU fits within 0.00931mm2 of silicon and operates at 3GHz while consuming 36.90mW of power. It is a negligible cost to modern server systems.

The advancement of general-purpose architecture has reached a juncture where the continuing investment to improve instruction per cycle (IPC) yields diminishing returns. While domain-specific architecture more efficiently converts silicon resources into throughput, economic viability hinders their wider adoption, except in a few areas. A more feasible way is to extract reusable operations that can be used across multiple domains and then find efficient architecture to support them. This dissertation focuses on operations involving pairs of keys and values. The application spans a wide range of domains, including database, graph computing, genomics, and sparse computing. The processing of key-value pairs is divided into two categories: ordered and unordered. For the ordered category, we optimized the general merge style operations on a sorted key-value array by creating a set of highly composable primitives. Next, we show that many widely used ordered data structures and algorithms, such as heap and binary search, can be accelerated by rewriting them to use merge operation as a building block. For the unordered ones, we observe that reduce-by-key is a common bottleneck in many domains. We propose the design of the Reduce-By-Key core and introduce a new algorithm to accelerate this operation. We also analytically prove that our method is close to optimal. Lastly, we investigate the decomposition operation on sparse tensors - a special form of key-value pairs. We show how a PE-interactive architecture can be used to significantly increase data reuse.

Speculative side-channel attacks have forced computer architects to rethink speculative execution. Effectively preventing microarchitectural state from leaking sensitive information will be a key requirement in future processor design. An important limitation of many secure speculation schemes is a reduction in the available memory parallelism, as unsafe loads (depending on the particular scheme) are blocked, as they might potentially leak information. Our contribution is to show that it is possible to recover some of this lost memory parallelism, by safely predicting the addresses of these loads in a threat-model transparent way, i.e., without worsening the security guarantees of the underlying secure scheme. To demonstrate the generality of the approach, we apply it to three different secure speculation schemes: Non-speculative Data Access (NDA), Speculative Taint Tracking (STT), and Delay-on-Miss (DoM). An address predictor is trained on non-speculative data, and can afterwards predict the addresses of unsafe slow-to-issue loads, preloading the target registers with speculative values, that can be released faster on correct predictions than starting the entire load process. This new perspective on speculative execution encompasses all loads, and gives speedups, separately from prefetching. We call the address-predicted counterparts of loads Doppelganger Loads. They give notable performance improvements for the three secure speculation schemes we evaluate, NDA, STT, and DoM. The Doppelganger Loads reduce the geometric mean slowdown by 42%, 48%, and 30% respectively, as compared to an unsafe baseline, for a wide variety of SPEC2006 and SPEC2017 benchmarks. Furthermore, Doppelganger Loads can be efficiently implemented with only minor core modifications, reusing existing resources such as a stride prefetcher, and most importantly, requiring no changes to the memory hierarchy outside the core.

The General-Purpose GPU (GPGPU) has emerged as the predominant computing device for extensive parallel workloads in the fields of Artificial Intelligence (AI) and Scientific Computing, primarily owing to its adoption of the Single Instruction Multiple Thread architecture, which not only provides a wealth of thread context but also effectively hide the latencies exposed in the single threads executions. As computational demands have evolved, modern GPGPUs have incorporated specialized matrix engines, e.g., NVIDIA’s Tensor Core (TC), in order to deliver substantially higher throughput for dense matrix computations compared with traditional scalar or vector architectures. Beyond mere throughput, energy efficiency is a pivotal concern in GPGPU computing. The register file is the largest memory structure on the GPGPU die and typically accounts for over 20% of the dynamic power consumption. To enhance energy efficiency, GPGPUs incorporate a technique named register caching borrowed from the realm of CPUs. Register caching captures temporal locality among register operands to reduce energy consumption within a 2- level register file structure. The presence of TC raises new challenges for Register Cache (RC) design, as each matrix instruction applies intensive operand delivering traffic on the register file banks. In this study, we delve into the RC design trade-offs in GPGPUs. We undertake a comprehensive exploration of the design space, encompassing a range of workloads. Our experiments not only reveal the basic design considerations of RC but also clarify that conventional caching strategies underperform, particularly when dealing with TC computations, primarily due to poor temporal locality and the substantial register operand traffic involved. Based on these findings, we propose an enhanced caching strategy featuring a look-ahead allocation policy to minimize unnecessary cache allocations for the destination register operands. Furthermore, to leverage the energy effici, Den allmänna ändamålsgrafikprocessorn (GPGPU) har framträtt som den dominerande beräkningsenheten för omfattande parallella arbetsbelastningar inom områdena för artificiell intelligens (AI) och vetenskaplig beräkning, huvudsakligen tack vare dess antagande av arkitekturen för enkel instruktion, flera trådar (Single Instruction Multiple Thread), vilket inte bara ger en mängd trådcontext utan också effektivt döljer de latenser som exponeras vid enskilda trådars utförande. När beräkningskraven har utvecklats har moderna GPGPU:er inkorporerat specialiserade matrismotorer, t.ex., NVIDIAs Tensor Core (TC), för att leverera avsevärt högre genomströmning för täta matrisberäkningar jämfört med traditionella skalär- eller vektorarkitekturer. Bortom endast genomströmning är energieffektivitet en central oro inom GPGPUberäkning. Registerfilen är den största minnesstrukturen på GPGPU-dien och svarar vanligtvis för över 20% av den dynamiska effektförbrukningen För att förbättra energieffektiviteten inkorporerar GPGPU:er en teknik vid namn registercachning, lånad från CPU-världen. Registercachning fångar temporal lokalitet bland registeroperanderna för att minska energiförbrukningen inom en 2-nivåers registerfilstruktur. Närvaron av TC innebär nya utmaningar för Register Cache (RC)-design, eftersom varje matrisinstruktion genererar intensiv operandleverans på registerfilbankarna. I denna studie fördjupar vi oss i RC-designavvägandena i GPGPU:er. Vi genomför en omfattande utforskning av designutrymmet, som omfattar olika arbetsbelastningar. Våra experiment avslöjar inte bara de grundläggande designövervägandena för RC utan klargör också att konventionella cachestrategier underpresterar, särskilt vid hantering av TC-beräkningar, främst på grund av dålig temporal lokalitet och den betydande trafiken med registeroperand. Baserat på dessa resultat föreslår vi en förbättrad cachestrategi med en look-ahead-alloceringspolicy för att minimera onödiga cacheallokeringar för destinationens re

Future high-density and high channel count neural interfaces that enable simultaneous recording of tens of thousands of neurons will provide a gateway to study, restore and augment neural functions. However, building such technology within the bit-rate limit and power budget of a fully implantable device is challenging. The wired-OR compressive readout architecture addresses the data deluge challenge of a high channel count neural interface using lossy compression at the analog-to-digital interface. In this article, we assess the suitability of wired-OR for several steps that are important for neuroengineering, including spike detection, spike assignment and waveform estimation. For various wiring configurations of wired-OR and assumptions about the quality of the underlying signal, we characterize the trade-off between compression ratio and task-specific signal fidelity metrics. Using data from 18 large-scale microelectrode array recordings in macaque retina ex vivo, we find that for an event SNR of 7-10, wired-OR correctly detects and assigns at least 80% of the spikes with at least 50× compression. The wired-OR approach also robustly encodes action potential waveform information, enabling downstream processing such as cell-type classification. Finally, we show that by applying an LZ77-based lossless compressor (gzip) to the output of the wired-OR architecture, 1000× compression can be achieved over the baseline recordings., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Bio-Electronics

The rise of chronic disease patients and the pandemic pose immediate threats to healthcare expenditure and mortality rates. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, leveraging the recent advances of Internet of Things and smart sensors. Meanwhile, reinforcement learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for distinct applications and services. Thus, this article presents a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. It can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health. Specifically, we first present an overview of the I-health systems' challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, deep RL (DRL), and multiagent RL models. We highlight important guidelines on how to select the appropriate RL model for a given problem, and provide quantitative comparisons, showing the results of deploying key RL models in two scenarios that can be followed in monitoring applications. After that, we conduct an in-depth literature review on RL's applications in I-health systems, covering edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and future research directions to enhance RL's success in I-health systems, which opens the door for exploring some interesting and unsolved problems., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Networked Systems

Computation-in-Memory (CIM) is a promising alternative to traditional computing systems where the storage is conceptually separated fromthe computing units. Instead, the CIM paradigm aims to perform the computation where the data resides, alleviating the memory bottleneck and ultimately leading to higher energy efficiency and performance. From the memory technology perspective, memristors, emerging non-volatile memory devices, demonstrate various beneficial characteristics. Although the concept of CIM, in combination with these emerging memory technologies, is in the infancy stage, it shows great potential as a future of computing systems. To further understand and quantify the potential of CIM, more development is required at each abstraction level. In this thesis, we first explore the main potentials for memristor-based computation-in-memory. Then, we study different applications from the CIM perspective to understand different behaviors and patterns of applications and use this knowledge to develop architectural solutions for CIM. Based on that, we study the realization of CIM as a generic and flexible platformat amicro-architecture level., Computer Engineering

In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12 th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.

Functional safety-relevant edge AI systems are the cutting of AI research, and the technological gap is significant. In this presentation we approach the issue of creation and deployment of both typical AI applications and functional safety systems. We pay particular attention to the merging of the two workflows and present options and ideas to achieve this.

In the past few years, Convolutional Neural Networks (CNN) have demonstrated promising performance in various real-world cybersecurity applications, such as network and multimedia security. However, the underlying fragility of CNN structures poses major security problems, making them inappropriate for use in security-oriented applications, including computer networks. Protecting these architectures from adversarial attacks necessitates using security-wise architectures that are challenging to attack. In this study, we present a novel architecture based on an ensemble classifier that combines the enhanced security of 1-Class classification (known as 1C) with the high performance of conventional 2-Class classification (known as 2C) in the absence of attacks. Our architecture is referred to as the 1.5-Class (cmb-classifier) classifier and is constructed using a final dense classifier, one 2C classifier (i.e., CNNs), and two parallel 1C classifiers (i.e., auto-encoders). In our experiments, we evaluated the robustness of our proposed architecture by considering eight possible adversarial attacks in various scenarios. We performed these attacks on the 2C and cmb-classifier architectures separately. The experimental results of our study showed that the Attack Success Rate (ASR) of the I-FGSM attack against a 2C classifier trained with the N-BaIoT dataset is 0.9900. In contrast, the ASR is 0.0000 for the cmb-classifier., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Cyber Security

Over the last decade, applications like self-driving, image recognition and speech processing are having more and more impact on the society, all these applications are based on machine learning, and machine learning is all about metrics and vectors. For that reason, vector processors are getting attraction again. Most of the previous research of vector processor focuses on single-core performance, and most of today’s large-scale computer system use non-uniform memory access (NUMA) architecture, how to efficiently deploy the vector processor in a NUMA environment remains a problem. NUMA is a shared memory model, with a NUMA system, there are multiple memories distributed in the system and usually each NUMA node has one memory. It will take the processors longer to access memories in other nodes then the memory in the same node, and this feature shows some opportunities to increase the performance of the NUMA system by accelerating the remote memory accesses. In this thesis, a subset of the PARSEC benchmark are vectorized for both ARM Scalable Vector Instructions (SVE) and RISC-V Vector Instructions (RVV), and the single-core performance of these two types of processors will be compared based on this benchmark. Then a NUMA system is made for ARM SVE and the memory access pattern is analysed with gem5 simulator., Computer Engineering