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IEEE 802.11ax is the standard for the new generation WiFi networks. In this paper, we formulate the problem of joint access point (AP) placement and power-channel-resource unit assignment for 802.11ax-based dense WiFi. The objective is to minimize the number of APs. Two quality-of-service (QoS) requirements are to be fulfilled: (1) a two-tier throughput requirement which ensures that the throughput of each station is good enough, and (2) a fault tolerance requirement which ensures that the stations could still use WiFi even when some APs fail. We prove that this problem is NP-hard. To tackle this problem, we first develop an analytic model to derive the throughput of each station under the OFDMA mechanism and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results under both fixed-user and mobile-user cases show that: (1) when the area is small (50 x 50 m2), our algorithm gives the optimal solutions; when the area is larger (80 x 60 m2), our algorithm can reduce the number of APs by 34.9-87.7% as compared to the Random and Greedy algorithms. (2) Our algorithm can always get feasible solutions that fulfill the QoS requirements.

This article presents a quick overview of conventional precoding and beamforming techniques in the cellular networks and proposes a novel coordinated beamforming technique for coordinated multipoint (CoMP) networks, which is based upon signal-to-leakage-plus-noise ratio (SLNR) metric. The proposed scheme has reduced computational cost and offers improved performance compared to the conventional multi-cell precoding schemes in the noise-dominated environments. Simulation results reveal that proposed scheme offers a spectrally efficient, low complexity solution with improved average user throughput and sum rate in the CoMP enabled networks.

Mobile edge computing achieves better application responsiveness by offloading tasks from end devices to edge servers installed at the vicinity. Practical scenarios such as post-disaster rescuing and battlefield monitoring make it attractive to use end devices themselves as edge servers. This however introduces a new challenge: Due to mobility and power limitation, the set of edge servers becomes dynamic. As some servers fail, the tasks that run on them will also fail. This paper introduces a new dynamic edge computing model and conducts the first study on robust task offloading which is tolerant to h server failures. We propose online primal-dual algorithms that offload tasks as they arrive. We evaluate the performance of our robust task offloading solutions through extensive simulations based on real task sets. The results show that our proposed solutions can well handle edge dynamics and achieve near optimal throughput (above 95%) compared to the optimal offline benchmark algorithm.

In this paper, a novel method is proposed to design a hybrid network (HN) to increase isolation and shape radiation patterns for multiple-input multiple-output (MIMO) antenna systems. The HN is a combination of a decoupling feeding network (DFN) and a defected ground network (DGN), which are populated by several surface-mounted reactive components whose reactances are determined by the N-ary optimization algorithm. Two decoupling examples are presented to validate the design methodology and elaborate on the design procedure. Measurement results show that the HN helps to realize impedance matching with reflection coefficients below -10 dB, isolation improvement from -5.4 dB/-8.9 dB to below -20 dB, and low envelope correlation coefficient below 0.06 for MIMO antennas with the element separation of 0.160/0.240. Moreover, in both examples, the decoupling case with omnidirectional radiation patterns achieves a better throughput performance, compared with another decoupling case with directional radiation. In comparison to prior decoupling networks only focusing on isolation enhancement, the proposed HN achieves high isolation and desired radiation patterns simultaneously with a very accurate design methodology.

Wireless Sensor Networks include a substantial number of nodes with limited battery power that is used for gathering and sending the data to the Base station. Here most energy is consumed in data transfer. Hence a foremost problem is the maximization of network lifetime through minimization of energy consumption in the nodes. To resolve this issue, the clustering method is involved to achieve energy-efficient data transmission. A cluster head is elected which uses an energy distribution mechanism to conserve the remaining power, thereby extending network lifetime. Various present methodologies are employed but every algorithm had notable constraints individually. In this paper, a hybrid Sparrow Search Algorithm with Differential Evolution algorithm is intended to solve the energy efficiency issue by cluster head selection in Wireless Sensor Networks. The proposed algorithm uses the high-level search efficiency of the Sparrow Search Algorithm and the lively potential of Differential Evolution that enhances the lifetime of nodes. The performance of this hybrid model seems to be exploiting the count of alive nodes and dead nodes, throughput, and residual energy. The proposed Improved Sparrow search algorithm using the Differential evolution model for choosing the best possible cluster head shows a development in residual power and throughput than other compared algorithms.

Mobile Cloud Computing enables the migration of services to the edge of the Internet. Therefore, high-performance computing clusters are widely deployed to improve computational capabilities of such environments. However, they are prone to failures and need analytical models to predict their behaviour in order to deliver desired quality-of-service and quality-of-experience to mobile users. This paper proposes a 3D analytical model and a problem-solving approach for sustainability evaluation of high-performance computing clusters. The proposed solution uses an iterative approach to obtain performance measurements to overcome the state space explosion problem. The availability modelling and evaluation of master and computing nodes are performed using a multi-repairman approach. The optimum number of repairmen is also obtained to get realistic results and reduce the overall cost. The proposed model is validated using discrete event simulation. The analytical approach is much faster and in good agreement with the simulations. The analysis focuses on mean queue length, throughput, and mean response time outputs. The maximum differences between analytical and simulation results in the considered scenarios of up to a billion states are less than1.149%,3.82%, and3.76%respectively. These differences are well within the5%of confidence interval of the simulation and the proposed model.

Product rate and grade transitions are two significant operations undertaken by process plants to operate in uncertain environments. Rate transitions, typically dictated by economic considerations, are often implemented in open loop via a throughput manipulator (TPM). The absence of feedback of product rate to manipulate the TPM results in an offset during rate/grade transitions as well as in presence of feed-side disturbances. In this work, we explore use of a simple PI controller based supervisory control layer that guides the plantwide regulatory layer to achieve product rate targets during grade transition or in the presence of feed grade disturbances. While such a layer cannot incorporate optimal operation and constraint handling explicity as in model predictive control, it obviates the need for advanced process control elements that medium-to-small scale industries can ill-afford The PI based supervisory-regulatory control structure is illustrated using simulated case studies on a prototypical process for diacetone alcohol production comprising of a reactor followed by two distillation columns and a recycle.

Neuromorphic computing chips consisting of crossbar arrays of emergent non-volatile memory have the potential of achieving both high energy efficiency and throughput as low power implementation of convolutional neural network (CNN) inference engines. However, such hardware has design constraints such as its limited fan-in/fan-out and resource-inefficient mapping that make the design and deployment of CNN on them challenging. As a result, the user has to design the CNN model with intricate knowledge of the hardware architecture and even cannot fit the models in the hardware for CNN with high resolution image input. In this paper, we propose the use of aggregated sub-nets, NC-net, which is a constrained form of the traditional layer structure, to solve these issues. With our method, we put forward an energy-efficient buffer-and ADC/DAC-free (Digital-to-Analogue Converter and Analogueto-Digital Converter) architecture and a scalable end-to-end solution that automatically satisfies the hardware constraints of crossbar architectures, while optimizing the resource usage. In our solution, the exploration and deployment of a CNN for a neuromorphic crossbar hardware starts with a design front-end based on TensorFlow. Our automated design flow maps the NCnet network from TensorFlow to the crossbar architecture. We tested our designs on both a simulator and a field-programmable gate array (FPGA) emulator with various benchmarks. In addition to general benchmarks including MNIST, SVHN, CIFAR-10 and CIFAR-100, we tested our system on a real-world application, human detection with high resolution (224×224) images as input. Our system achieves the state-of-the-art accuracy for these benchmarks on the crossbar-based neuromorphic hardware, with an accuracy of more than 90% for the latter. It also yielded up to 4:25× improvement in the efficiency of spiking core usage compared to TrueNorth.

Cancer is characterized by an extremely complex biological background, which hinders personalized therapeutic interventions. Precision medicine promises to overcome this obstacle through integrating information from different ‘subsystems’, including the host, the external environment, the tumor itself and the tumor micro-environment. Immunogenetics is an essential tool that allows dissecting both lymphoid cancer ontogeny at both a cell-intrinsic and a cell-extrinsic level, i.e. through characterizing micro-environmental interactions, with a view to precision medicine. This is particularly thanks to the introduction of powerful, high-throughput approaches i.e. next generation sequencing, which allow the comprehensive characterization of immune repertoires. Indeed, NGS immunogenetic analysis (Immune-seq) has emerged as key to both understanding cancer pathogenesis and improving the accuracy of clinical decision making in oncology. Immune-seq has applications in lymphoid malignancies, assisting in the diagnosis e.g. through differentiating from reactive conditions, as well as in disease monitoring through accurate assessment of minimal residual disease. Moreover, Immune-seq facilitates the study of T cell receptor clonal dynamics in critical clinical contexts, including transplantation as well as innovative immunotherapy for solid cancers. The clinical utility of Immune-seq represents the focus of the present contribution, where we highlight what can be achieved but also what must be addressed in order to maximally realize the promise of Immune-seq in precision medicine in cancer.

Blockchain is starting to be deployed in the Internet of Things (IoT) to enable autonomous device-to-device transactions. However, traditional block-based blockchain techniques, such as Bitcoin and Ethereum, are not suitable for IoT environments due to their low throughput, high computation overhead, and costly transaction fee. To satisfy the requirements of IoT environments, Directed-Acyclic-Graph (DAG) based approaches, aiming to provide cheap blockchain services with low latency and high throughput, are emerging. This paper presents a set of comprehensive experimental studies on IOTA, a representative DAG-based blockchain. We aim to exhibit its unique characteristics mainly from three aspects: performance, security, and system robustness. We have developed a series of benchmark tools and judiciously selected typical configurations to perform experimental examinations with a real private IOTA network. Our studies reveal several interesting findings: 1) The throughput of IOTA is higher than the traditional block-based blockchain but far less than the reported thousands of transactions per second (TPS) in its whitepaper, even with scaling-up configurations; 2) The database query heavily impacts the performance of IOTA, even more than its mining (i.e., Proof of Work, PoW) process; 3) The system robustness of IOTA is closely related to the frequency of the incoming transactions while the milestone sent by the centralized coordinator has little effect on the system robustness. We make our benchmark tools public and expect our works can inspire system architects, application designers, and practitioners with new optimization directions and potential application cases for further exploration.

Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a growing demand for hardware accelerators that accommodate a variety of CNNs to improve their inference latency and energy efficiency, in order to enable their deployment in real-time applications. Among popular platforms, field-programmable gate arrays (FPGAs) have been widely adopted for CNN acceleration because of their capability to provide superior energy efficiency and low-latency processing, while supporting high reconfigurability, making them favorable for accelerating rapidly evolving CNN algorithms. This article introduces a highly customized streaming hardware architecture that focuses on improving the compute efficiency for streaming applications by providing full-stack acceleration of CNNs on FPGAs. The proposed accelerator maps most computational functions, that is, convolutional and deconvolutional layers into a singular unified module, and implements the residual and concatenative connections between the functions with high efficiency, to support the inference of mainstream CNNs with different topologies. This architecture is further optimized through exploiting different levels of parallelism, layer fusion, and fully leveraging digital signal processing blocks (DSPs). The proposed accelerator has been implemented on Intel's Arria 10 GX1150 hardware and evaluated with a wide range of benchmark models. The results demonstrate a high performance of over 1.3 TOP/s of throughput, up to 97% of compute [multiply-accumulate (MAC)] efficiency, which outperforms the state-of-the-art FPGA accelerators.

Microdroplets and their dispersion, with a large specific surface area and a short diffusion distance, have been applied in various unit operations and reaction processes. However, it is still a challenge to control the size and size distribution of microdroplets, especially for high-throughput generation. In this work, a novel ultra-high speed rotating packed bed (UHS-RPB) was invented, in which rotating foam packing with a speed of 4000–12000 r·min–1 provides microfluidic channels to disperse liquid into microdroplets with high throughput. Then generated microdroplets can be directly dispersed into a continuous falling film for obtaining a mixture of microdroplet dispersion. In this UHS-RPB, the effects of rotational speed, liquid initial velocity, liquid viscosity, liquid surface tension and packing pore size on the average size (d32) and size distribution of microdroplets were systematically investigated. Results showed that the UHS-RPB could produce microdroplets with a d32 of 25–63 μm at a liquid flow rate of 1025 L·h–1, and the size distribution of the microdroplets accords well with Rosin–Rammler distribution model. In addition, a correlation was established for the prediction of d32, and the predicted d32 was in good agreement with the experimental data with a deviation within ±15%. These results demonstrated that UHS-RPB could be a promising candidate for controllable preparation of uniform microdroplets.

Background: High throughput screening systems are automated labs for the analysis of many biochemical substances in the drug discovery and virus detection process. This paper was motivated by the problem of automating testing for viruses and new drugs using high throughput screening systems. The emergence of severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) at the turn of 2019-2020 presented extraordinary challenges to public health. Existing approaches to test viruses and new drugs do not use optimal schedules and are not efficient. Objective: The scheduling of activities performed by various resources in a high throughput screening system affects its efficiency, throughput, operations cost, and quality of screening. This study aims to minimize the total screening (flow) time and ensure the consistency and quality of screening. Methods: This paper develops innovative mixed-integer models that efficiently compute optimal schedules for screening many microplates to identify new drugs and determine whether samples contain viruses. The methods integrate job-shop and cyclic scheduling. Experiments are conducted for a drug discovery process of screening an enzymatic assay and a general process of detecting SARS-CoV-2. Results: The method developed in this article can reduce screening time by as much as 91.67%. Conclusion: The optimal schedules for high throughput screening systems greatly reduce the total flow time and can be computed efficiently to help discover new drugs and detect viruses.

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. The framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. We present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.

Due to recent explosion of data volume and velocity, a new array of lightweight key-value stores have emerged to serve as alternatives to traditional databases. The majority of these storage engines, however, sacrifice their read performance in order to cope with write throughput by avoiding random disk access when writing a record in favor of fast sequential accesses. But, the boundary between sequential vs. random access is becoming blurred with the advent of solid-state drives (SSDs). In this work, we propose our new key-value store, LogStore, optimized for hybrid storage architectures. Additionally, introduce a novel cost-based data staging model based on log-structured storage, in which recent changes are first stored on SSDs, and pushed to HDD as it ages, while minimizing the read/write amplification for merging data from SSDs and HDDs. Furthermore, we take a holistic approach in improving both the read and write performance by dynamically optimizing the data layout, such as deferring and reversing the compaction process, and developing an access strategy to leverage the strengths of each available medium in our storage hierarchy. Lastly, in our extensive evaluation, we demonstrate that LogStore achieves up to 6x improvement in throughput/latency over LevelDB, a state-of-the-art key-value store

A spectrum of emerging applications including edge artificial intelligence advocate the precompute-and-search scheme with embedded small-size content addressable memory (CAM) for its hardware efficiency instead of repetitive arithmetic operations. However, integration of the CAM macros that are conventionally implemented with full custom analog circuits renders design-space exploration and optimization to be difficult at system level. As an alternative, a complete design flow for compiling ternary CAM on chip using foundry-supplied digital standard cells is introduced in this paper. Based on novel CAM architecture and logic design, we leverage guided placement and routing with mainstream EDA tools for exploiting the inherent structure regularity of CAM-cell arrays in layout. An analytical model is also presented which allows us for a systematic investigation on the energy reduction by adapting our design to various pre-search structures. Validated on a post-layout 32W64 ternary CAM in 28nm, our parallel-matching scheme performs at 2.6GHz with 0.42fJ/bit/search, and the (8-bit) pre-search scheme achieves 0.19fJ/bit/search at 1.1GHz, both under the 0.9V supply voltage. In addition to flexibility for tradeoffs between the search throughput and energy, our all-digital CAM design enables voltage scaling aggressively down to 0.45V with a minimum energy consumption of 0.06fJ/bit/search at 50MHz.

Resistive-RAM (RRAM) based deep neural network (DNN) accelerator has shown great potential as it is good at the matrix-vector multiplication (MVM) operator. However, it does not benefit non-MVM operators, such as transcendental activation or element-wise operations, which often require customized CMOS circuits in conventional DNN accelerator designs. In this paper, we propose a new RRAM-based DNN inference accelerator, which leverages the proposed RRAM-CORDIC and RRAM-MLP algorithms to make the transcendental and element-wise operators calculable in the RRAM crossbar just like MVM. Both algorithms can exploit the higher MAC (multiply-and-accumulation) parallelism that is traditionally expensive in CMOS but now efficient in the RRAM crossbar. Then we further propose an inter-crossbar pipelining scheme, which can balance the number of crossbars for MVM and non-MVM operations and orchestrate them in pursuing higher DNN computing throughput. The experiment results show that both algorithms can sustain a high arithmetic accuracy, and deliver less than 1% DNN accuracy loss on typical inference workloads. The elimination of expensive CMOS circuits, in turn, can trade more crossbar resources in the same area to speed up the performance by 1.16X to 2.33X. With the extended operators, the RRAM-based DNN accelerator can switch crossbar functions at will, and apply for a diverse of DNN models in a unified in-memory accelerator architecture.

In this paper, we study Space-Air-Ground integrated Vehicular Network (SAGVN), and propose an online control framework to dynamically slice the SAG spectrum resource for isolated vehicular services provisioning. In particular, at a given time slot, the system makes online decisions on the request admission and scheduling, UAV dispatching, and resource slicing for different services. To characterize the impact of those parameters, we construct a time-averaged queue stability criteria by taking queue backlogs of all services into consideration, and formulate a system revenue function which incorporates the time-averaged system throughput and UAV dispatching cost. The objective is to maximize the system revenue while stabilizing the time-averaged queue, which falls into the scope of Lyapunov optimization theory. By bounding the drift-plus-penalty, the original problem can be decoupled into four independent subproblems, each of which is readily solved. The merits of our control framework are three-fold: 1) the system is able to admit and process as many requests as possible (i.e., maximizing the time-averaged throughput); 2) the time-averaged UAV dispatching cost is minimized; and 3) service queues are stabilized in the long-term. Extensive simulations are carried out, and the results demonstrate that the control framework can effectively achieve the system revenue maximization and queueing stabilization. Moreover, it can balance the trade-off among system throughput, UAV dispatching cost, and queueing states via parameter tuning. Compared with the fixed slicing, our dynamic slicing can react to the vehicular environment rapidly and achieve an average 26% of throughput improvement.

The ability of Convolutional Neural Networks (CNNs) to accurately process real-time telemetry has boosted their use in safety-critical and high-performance computing systems. As such systems require high levels of resilience to errors, CNNs must execute correctly in the presence of hardware faults. Full duplication provides the needed assurance but incurs a prohibitive 100% overhead. Algorithmic techniques are known to offer low-cost solutions, but the practical feasibility and performance of such techniques have never been studied for CNN deployment platforms (e.g., TensorFlow or TensorRT on GPUs). In this paper, we focus on algorithmically verifying Convolutions, which are the most resource-demanding operations in CNNs. We use checksums to verify convolutions, adding a small amount of redundancy, far less than full-duplication. We first identify the challenges that arise in employing Algorithm-Based Error Detection (ABED) for Convolutions in optimized inference platforms that fuse multiple network layers and use reduced-precision operations, and demonstrate how to overcome them. We propose and evaluate variations of ABED techniques that offer implementation complexity, runtime overhead, and coverage trade-offs. Results show that ABED can detect all transient hardware errors that might otherwise corrupt output and does so while incurring low runtime overheads (6-23%), offering at least 1.6X throughput to workloads compared to full duplication.

An integrated space-terrestrial network based on the ultra-dense low-earth-orbit (LEO) satellite constellations has been envisioned in both 5G and beyond 5G (B5G) networks. This approach is a powerful solution to some key challenges from Internet-of-Thing (IoT) services, such as the lack of link capacity to deal with large data transfer or coverage in the remote areas. This paper focuses on the beamforming design for the transmissions from multiple LEO satellites, equipped with massive phased array antenna, to a large number of heterogeneous terrestrial terminals. Superposition coding based beamforming is efficient in dealing with the receiver heterogeneity, but at the cost of higher computational complexity. Based on the dual decomposition theory as well as deep-neural-networks (DNNs), this paper proposes to combine the non-linear approximation ability of DNNs with distributed algorithms. This combination not only supports advanced non-orthogonal beamforming algorithms for achieving superior throughput performance, but also keeps the overall computational complexity low and enables the beamforming process to be speed up dramatically through parallel computing.

This paper proposes an efficient structure-aware storage system, called GraphSO. It can be integrated into existing out-of-core graph processing systems to promote the execution efficiency of concurrent jobs with lower I/O overhead. Specifically, it logically divides the partitions of existing graph processing systems into a series of small same-sized chunks. At runtime, these small chunks with active vertices are judiciously loaded by GraphSO to construct new logical partitions for existing graph processing systems to handle, where the most-frequently-used chunks are preferentially loaded to construct the logical partitions and the other ones are delayed to wait to be required by more jobs. In this way, it can effectively spare the cost of loading the graph data associated with the inactive vertices with low repartitioning overhead and can also enable the loaded graph data to be fully shared by concurrent jobs. Moreover, GraphSO also designs a buffering strategy to efficiently cache the most-frequently-used chunks in the main memory to further minimize the I/O traffic by avoiding repeated load of them. Experimental results show that GraphSO improves the throughput of GridGraph, GraphChi, X-Stream, DynamicShards, LUMOS, Graphene, and Wonderland by 1.4-3.5, 2.1-4.3, 1.9-4.1, 1.9-2.9, 1.5-3.1, 1.3-1.5, and 1.3-2.7 times after integrating with them, respectively.

The effects of model parameter uncertainty on traffic flow control problems have recently drawn research attention. While the uncertainty in fundamental diagram related parameters has been investigated in the past, few articles have focused on network parameters uncertainty, including turning ratio uncertainty. To fill this gap, this article proposes a robust control model to deal with the uncertainties in the turning ratio by using distributionally robust chance constraints. The model allows one to compute the optimal control action that maximizes some objective, under all possible distributions of network parameters. We then apply this robust control framework to both a freeway network and an urban network, and evaluate the impact of uncertainty on optimal control inputs, over the test networks. The case studies show that compared to non-robust control, the proposed robust model can reduce congestion brought by the uncertainties and improve the overall throughput., Accepted for publication on IEEE Transactions on Intelligent Transportation Systems

Leveraging cloud computing services to improve the flexibility and scalability of the IT services delivered to end-users is becoming increasingly important. However, organizations using cloud computing services face many challenges: decreased visibility into the performance of services being delivered to their end-users. Organizations using cloud computing services need to have visibility not only into the performance of applications that have moved to the cloud, but also into the different computing resources on which these applications depend. The monitoring system monitors the running state of virtual machines, response time and throughput. By analysing this data, you can identify the causes of service delays. During the operation of the monitoring system, virtual machines are suspended, a service delay occurs in order to avoid this delay. Therefore in this paper, a cloud computing model is proposed that takes into account the time spent for monitoring, calculations and probabilities of delay and waiting time are made, blocking probabilities are also put to account and results are shown in diagrams and the effects are discussed.

Sparse convolution neural networks (CNNs) are promising in reducing both memory usage and computational complexity while still preserving high inference accuracy. State-of-the-art sparse CNN accelerators can deliver high throughput by skipping zero weights and/or activations. To operate on only nonzero weights and activations, sparse accelerators typically search pairs of nonzero weights and activations for multiplication-accumulation (MAC) operations. However, the conventional search operation results in a severe limitation in the PE array scale because of the enormous demands of internal interconnection and memory bandwidth. In this paper, we first provide a design principle to free the search process of sparse CNN accelerations. Specifically, the indexes of the static compressed weights access the dynamic activations directly to avoid the search process for MAC operations. We then develop two search-free inference accelerators, called Swan and Swan-flexible, for sparse CNN accelerations. Swan supports search-free sparse convolution accelerations for interconnection and bandwidth saving. Compared with Swan, Swan-flexible not only has the search-free capability but also comprises a configurable architecture for optimum throughput. We formulate a mathematical optimization problem by combining the configurable characterization with the compressive dataflow to optimize the overall throughput. Evaluations based on a place-and-route process show that the proposed designs, in a compact factor of 4096 PEs, achieve 1.5–2.7× higher speedup and 6.0–13.6× better energy efficiency than representative accelerator baselines with the same PE array scale.

Nowadays, the improvement of the data acquisition capability of the IIoT (Industrial Internet-of-Things) systems has brought about higher data throughput. Users can deploy industrial big data analysis applications on cloud platforms in a pay-as-you-go way to deal with the unstable data generation and data analysis workloads in the IIoT scenario. To procure stable and flexible computing resources, as a traditional pay-as-you-go cloud instance procurement option, on-demand instances are widely used, but their expensive prices also significantly increase users’ cost burden. To reduce the data analysis cost, in this paper, we establish a per-job cost-effective framework adopting spot instances, on-demand instances, and cloud storage for industrial big data analysis applications. In our framework, we take advantage of spot instances, which are computing instances provided at low prices under a pay-as-you-go model, to achieve cost savings. However, using spot instances carries the risk of being interrupted. Therefore, we propose to use a checkpointing mechanism to back up intermediate results to cloud storage to reduce the potential loss caused by spot instance interruptions. Considering the time-sensitivity of industrial big data analysis applications, we use on-demand instances as alternative computing resources after spot instance interruptions to ensure that users’ jobs can be completed without high time latencies. Evaluation results show that our framework can achieve cost savings as well as minimize time latencies for users’ jobs.

Phase Change Memory (PCM) can directly connect persistent memory to main memory bus, while it achieves high read throughput and low standby power, the critical concerns are its poor write performance and limited durability. A naturally in-spired design is the hybrid memory architecture that fuses DRAM and PCM, so as to exploit the positive aspects of both types of memory. Unfortunately, existing solutions are seriously challenged by the limited main memory size, which is the primary bottleneck of in-memory computing. In this paper, we introduce a novel Content Aware Hybrid DRAM-PCM memory system framework—CAHRAM, which exploits deduplication to improve line sharing with high memory efficiency. It reduces write traffic to hybrid memory by removing unnecessary duplicate line writes, thereby further enhancing the write endurance of PCM. And it also substantially extends available free memory space by coalescing redundant lines in hybrid memory. We also design a reference-based page migration technique to minimize the access overheads caused by the performance gap between DRAM and PCM. Compared with the state-of-the-art in a hybrid memory simulator, our experiment results show that CAHRAM can achieve the highest I/O performance and the longest PCM lifetime with the competitive efficiencies in space and energy.

Real-time in situ image analytics impose stringent latency requirements on intelligent neural network inference operations. While conventional software-based implementations on the graphic processing unit (GPU)-accelerated platforms are flexible and have achieved very high inference throughput, they are not suitable for latency-sensitive applications where real-time feedback is needed. Here, we demonstrate that high-performance reconfigurable computing platforms based on field-programmable gate array (FPGA) processing can successfully bridge the gap between low-level hardware processing and high-level intelligent image analytics algorithm deployment within a unified system. The proposed design performs inference operations on a stream of individual images as they are produced and has a deeply pipelined hardware design that allows all layers of a quantized convolutional neural network (QCNN) to compute concurrently with partial image inputs. Using the case of label-free classification of human peripheral blood mononuclear cell (PBMC) subtypes as a proof-of-concept illustration, our system achieves an ultralow classification latency of 34.2 [Formula: see text] with over 95% end-to-end accuracy by using a QCNN, while the cells are imaged at throughput exceeding 29 200 cells/s. Our QCNN design is modular and is readily adaptable to other QCNNs with different latency and resource requirements.

Today's blockchains suffer from low throughput and high latency, which impedes their widespread adoption of more complex applications like smart contracts. In this paper, we propose a novel paradigm for smart contract execution. It distinguishes between consensus nodes and execution nodes: different groups of execution nodes can execute transactions in parallel; meanwhile, consensus nodes can asynchronously order transactions and process execution results. Moreover, it requires no coordination among execution nodes and can effectively prevent livelocks. We show two ways of applying this paradigm to blockchains. First, we show how we can make Ethereum support parallel and asynchronous contract execution \emph{without hard-forks}. Then, we propose a new public, permissionless blockchain. Our benchmark shows that, with a fast consensus layer, it can provide a high throughput even for complex transactions like Cryptokitties gene mixing. It can also protect simple transactions from being starved by complex transactions.

Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a rapid (millisecond) time scale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion's gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we assembled a large-scale database of drug and drug metabolite CCS values using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors, achieving high prediction accuracies (0.8-2.2% median relative error on test set data). The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions.

Fluorescence laser-scanning microscopy (LSM) is experiencing a revolution thanks to the introduction of new asynchronous read-out single-photon (SP) array detectors. These detectors give access to an entirely new set of single-photon information typically lost in conventional fluorescence LSM, thus triggering a new imaging/spectroscopy paradigm – the so-called singlephoton LSM (SP-LSM). The revolution’s outcomes are, from one side, the blooming of new SP-LSM techniques and tailored SP array detectors; from the other side, the need for data acquisition (DAQ) systems effectively supporting such innovations. In particular, there is a growing need for DAQ systems capable of handling the high throughput and high temporal resolution information generated by the single-photon detectors. To fill this gap, we developed an open-source multi-channel time tagging module (TTM) based on a field-programmable-gate array (FPGA), that can temporally tag single-photon events – with 30 ps precision – and synchronisation events – with 4 ns precision. Furthermore, being an open-access project, the TTM can be upgraded, modified, and customized by the microscopy-makers. We connected the TTM to a fluorescence LSM equipped with a single-photon avalanche diode (SPAD) bidimensional array detector, and we implemented fluorescence lifetime image scanning microscopy (FLISM) and, for the first time, fluorescence lifetime fluctuation spectroscopy (FLFS). We expect that our BrigthEyes-TTM will support the microscopy community to spread SP-LSM in many life science labs.

Bispecific antibodies (BsAbs) represent an emerging class of immunotherapy but inefficiency in the current BsAb discovery paradigm has limited their broad clinical availability. Here we report a high throughput, agnostic, single-cell-based BsAb functional screening pipeline, comprising molecular and cell engineering for efficient generation of BsAb library cells, followed by functional interrogation at the single-cell level to identify and sort positive clones and downstream sequence identification with single-cell PCR and sequencing and functionality characterization. Using a CD19xCD3 bispecific T cell engager (BiTE) as a model system, we demonstrate that our single cell platform possesses a high throughput screening efficiency of up to one and half million variant library cells per run and can isolate rare functional clones at low abundance of 0.008%. Using a complex CD19xCD3 BiTE-expressing cell library with approximately 22,300 unique variants comprising combinatorially varied scFvs, connecting linkers and VL/VH orientations, we have identified 98 unique clones including extremely rare ones (∼ 0.001% abundance). We also discovered BiTEs that exhibit novel properties contradictory to conventional wisdom, including harboring rigid scFv connecting peptide linkers yet with in vitro cytotoxicity comparable to that of clinically approved Blinatumomab. Through sequencing analyses on sorted BiTE clones, we discovered multiple design variable preferences for functionality including the CD19VL-VH– CD3VH-VL and CD19VH-VL–CD3VH-VL arrangements being the most favored orientation. Sequence analysis further interrogated the sequence composition of the CDRH3 domain in scFvs and identified amino acid residues conserved for function. We expect our single cell platform to not only significantly increase the development speed of high quality of new BsAb therapeutics for cancer and other disorders, but also enable identifying generalizable design principles for new BsAbs and other immunotherapeutics based on an in-depth understanding of the inter-relationships between sequence, structure, and function.

Reinforcement learning (RL) has shown great performance in solving sequential decision-making problems. This paper proposes a framework to train RL agents conditioned on constraints that are in the form of structured language to improve training efficiency. We implemented an energy-efficient hardware accelerator to receive both images and text inputs that allows RL agents understand human language and act in real-world environments. A scalable and parallel hardware with different number of processing elements is implemented on both FPGA and ASIC that provides a balance between power consumption and performance. The post-layout ASIC design consumes 9.2 mW while providing 361 fps throughput.

Multi-modal services, typically integrated by visual, audio, and haptic signals, have been considered as promising killer services in 5G and beyond 5G era. However, due to essential difference among these signals, how to guarantee quality of multi-modal services is a significant technical challenge. Existing transmission schemes, which deliver and process each modality signal separately, cannot meet such requirements as low latency, high reliability, and high throughput. To get over the dilemma, this paper proposes a general cross-modal transmission strategy by taking advantage of the potential correlation among modalities, which consists of a delivery mechanism at the sender and a signal restoration procedure at the receiver. On the one hand, a visual-aided haptic content compression method is designed for the delivery mechanism. By utilizing the category correlation among modalities, haptic signals with similar visual content can be effectively compressed, reducing transmission burden. On the other hand, a fine-grained haptic to image synthesis (FHIS) approach is proposed for realizing signal restoration. Through exploring strong matching properties among modalities, the FHIS can restore the impaired, missing, delayed visual images from the received haptic signals. Experiments on a standard visual-haptic database and a practical platform evaluate the performance of the constructed cross-modal transmission strategy.

The cellular response to the complex extracellular microenvironment is highly dynamic in time and type of extracellular matrix. Accurately reconstructing this process and analyzing the changes in receptor conformation on the cell membrane surface and intracellular or intercellular signaling has been a major challenge in analytical chemistry and biophysical methodology. In this paper, a time-coded multi-concentration microfluidic chemical waveform generator was developed for the dynamic signaling probing with single-cell array of high temporal resolution, high throughput, and multi-concentration combination stimulation. Based on innovative microchannel structure, sophisticated external control methods and multiplexing technology, the system not only allowed for temporally sequential permutations of the four concentrations of stimuli (time code), but also generated pulsed and continuous waveforms at different frequencies in a highly controllable manner. Furthermore, the single-cell trap array was set up to efficiently capture cells in suspension, dramatically increasing throughput and reducing experiment preparation time. The maximum frequency of the platform was 1 Hz, and one cell could be stimulated at multiple frequencies. To show the ability of the system to investigate rapid biochemical events in high throughput, pulse stimulation and continuous stimulation of different frequencies and different time codes, combined with four concentrations of histamine (HA), were generated for probing G protein-coupled receptor (GPCR) signaling in HeLa cells. Then, statistical analysis was performed for the mean peak height and mean peak area of the cellular response. We believe that the time-coded multi-concentration microfluidic chemical waveform generator will provide a novel strategy for analytical chemistry, biophysics, cell signaling, and individualized medicine applications.

Taskflow aims to streamline the building of parallel and heterogeneous applications using a lightweight task graph-based approach. Taskflow introduces an expressive task graph programming model to assist developers in the implementation of parallel and heterogeneous decomposition strategies on a heterogeneous computing platform. Our programming model distinguishes itself as a very general class of task graph parallelism with in-graph control flow to enable end-to-end parallel optimization. To support our model with high performance, we design an efficient system runtime that solves many of the new scheduling challenges arising out of our models and optimizes the performance across latency, energy efficiency, and throughput. We have demonstrated the promising performance of Taskflow in real-world applications. As an example, Taskflow solves a large-scale machine learning workload up to 29% faster, 1.5x less memory, and 1.9x higher throughput than the industrial system, oneTBB, on a machine of 40 CPUs and 4 GPUs. We have opened the source of Taskflow and deployed it to large numbers of users in the open-source community., 18 pages, 24 figures

We consider a multichannel random access system in which each user accesses a single channel at each time slot to communicate with an access point (AP). Users arrive to the system at random and be activated for a certain period of time slots and then disappear from the system. Under such dynamic network environment, we propose a distributed multichannel access protocol based on multi-agent reinforcement learning (RL) to improve both throughput and fairness between active users. Unlike the previous approaches adjusting channel access probabilities at each time slot, the proposed RL algorithm deterministically selects a set of channel access policies for several consecutive time slots. To effectively reduce the complexity of the proposed RL algorithm, we adopt a branching dueling Q-network architecture and propose an efficient training methodology for producing proper Q-values over time-varying user sets. We perform extensive simulations on realistic traffic environments and demonstrate that the proposed online learning improves both throughput and fairness compared to the conventional RL approaches and centralized scheduling policies., 20 pages, 12 figures

This paper considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For DTM, we characterize a throughput-backlog trade-off of [O}(1/V), O(V)] to indicate that the system throughput goes up as the queue backlog increases. However, as V goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog. To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of ${[O(1/V), O((\log(V))^2)]}$ between the system throughput and the queue backlog.

This paper investigates intelligent reflecting surface (IRS) assisted wireless powered Internet of Things (IoT) networks. Specifically, multiple IoT devices first collect energy radiated from a power station (PS), then each device uses its harvested energy to support data transmission to an access point (AP) via frequency division multiple access (FDMA). In addition, an IRS aims to improve wireless energy transfer (WET) and wireless information transfer (WIT) capabilities using passive reflection beamformers. The system sum throughput, as a performance metric, is maximized evaluate the overall performance of the considered model, which is subject to the constraints of IRS phase shifts, transmission time scheduling, and bandwidth allocation. This problem is not convex with respect to multiple coupled variables, and cannot be directly solved. To circumvent this non-convexity, the transmission time scheduling and the bandwidth allocation are optimally designed in closed-form by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. Moreover, an alternating optimization (AO) algorithm is used to optimally design the IRS’s phase shifts during the WET and WIT phases in an alternating fashion. Specifically, we propose element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) algorithms to iteratively derive the optimal phase shifts in closed-form. We also characterize the convergence behaviour of the proposed algorithms. Finally, numerical results are presented to validate the performance of the proposed scheme, where the benefits of the IRS are highlighted in terms of sum throughput, transmission time scheduling, and energy harvesting, compared with the benchmark schemes.

Regular safety inspection is critical to reduce safety risk in industry. Applying consortium blockchain technology to safety inspection can ensure the effectiveness of inspection process and tracing of problems. However, there are two major issues when using conventional consortium blockchain. It is challenging to guarantee the authenticity of the retrieved data source, and meanwhile, achieving a balance between performance and security is not easy. Hence, this paper proposes a blockchain based performance-security balanced safety inspection framework (PSB-SIF), in which a safety inspection box is designed to ensure the authenticity of the inspector’s identity while inspection logic is executed automatically via smart contracts. In addition, this paper also proposes a novel credit scoring based Byzantine fault tolerant (BFT) consensus algorithm, named Safety Inspection Byzantine Fault Tolerance consensus algorithm (SIBFT), which is used to balance the performance and security of consensus network in safety inspection. We evaluate the proposed approach by comparing with the solutions using RAFT, Practical Byzantine Fault Tolerance (PBFT), and SIBFT consensus algorithms in terms of throughput, transaction latency, scalability and security of PSB-SIF. The evaluation results show that PSB-SIF is efficient for all these quality metrics.

A novel LEarning-based Spectrum Sensing and Access (LESSA) framework is proposed, wherein a cognitive radio (CR) learns a time-frequency correlation model underlying spectrum occupancy of licensed users (LUs) in a radio ecosystem; concurrently, it devises an approximately optimal spectrum sensing and access policy under sensing constraints. A Baum-Welch algorithm is proposed to learn a parametric Markov transition model of LU spectrum occupancy based on noisy spectrum measurements. Spectrum sensing and access are cast as a Partially-Observable Markov Decision Process, approximately optimized via randomized point-based value iteration. Fragmentation, Hamming-distance state filters and Monte-Carlo methods are proposed to alleviate the inherent computational complexity, and a weighted reward metric to regulate the trade-off between CR throughput and LU interference. Numerical evaluations demonstrate that LESSA performs within 5 percent of a genie-aided upper bound with foreknowledge of LU spectrum occupancy, and outperforms state-of-the-art algorithms across the entire trade-off region: 71 percent over correlation-based clustering, 26 percent over Neyman-Pearson detection, 6 percent over the Viterbi algorithm, and 9 percent over an adaptive Deep Q-Network. LESSA is then extended to a distributed Multi-Agent setting (MA-LESSA), by proposing novel neighbor discovery and channel access rank allocation. MA-LESSA improves CR throughput by 43 percent over cooperative TD-SARSA, 84 percent over cooperative greedy distributed learning, and 3x over non-cooperative learning via g-statistics and ACKs. Finally, MA-LESSA is implemented on the DARPA SC2 platform, manifesting superior performance over competitors in a real-world TDWR-UNII WLAN emulation; its implementation feasibility is further validated on a testbed of ESP32 radios, exhibiting 96 percent success probability., Comment: 33 pages, 9 figures, 1 table, Major Revisions under review at IEEE Transactions on Cognitive Communications and Networking (IEEE TCCN)

Hybrid energy supply composed of batteries and radio frequency (RF) signals has been anticipated to be a prominent solution for balancing the reliability and self-sustainability of future IoT networks. The newly emerging reconfigurable intelligent surface (RIS) is also capable of greatly enhancing spectral and energy efficiencies. In this paper, by considering an asynchronous transmission protocol among all energy receivers (ERs) and assuming the perfect self-interference cancellation (SIC) at the hybrid access point (HAP), we aim to maximize sum throughput in the RIS-aided hybrid powered communication networks (HPCNs) by jointly optimizing the transmit covariance matrices of the HAP and all ERs, the RIS reflection matrix and the downlink/uplink (DL/UL) time allocation. Generally, this optimization problem is intractable to solve due to strongly coupled variables and nonconvex unit-modulus constraints. To draw more insights into this joint design, we firstly carry out feasibility analysis on this problem, and then develop a 2-block alternating optimization algorithm, which consists of the semi-closed-form solution based iterative algorithm for deriving the optimal MIMO transceivers together with the DL/UL time allocation and the alternating direction method of multipliers (ADMM) based algorithm for the RIS design. To avoid the potential high complexity of alternating optimization, we also propose a two-stage scheme, where the RIS design is independent of the others and aims to create favorable DL/UL channels. The extension of our proposed algorithms to the practical imperfect SIC case is then discussed. Numerical results illustrate the superior performance of our proposed algorithms over the baselines in terms of the achievable sum throughput, and their time effectiveness in solving large-scale problems.

Future CPU-manycore heterogeneous systems can provide high peak throughput by integrating thousands of simple, independent, energy-efficient cores in a single die. However, there are two key challenges to translating this high peak throughput into improved end-to-end workload performance: (1) manycore co-processors rely on simple hardware putting significant demands on the software programmer; and (2) manycore co-processors use in-order cores that struggle to tolerate long memory latencies. To address the manycore programmability challenge, this paper presents a dense and sparse tensor processing framework based on PyTorch that enables domain experts to easily accelerate off-the-shelf workloads on CPUmanycore heterogeneous systems. To address the manycore memory latency challenge, we use our extended PyTorch framework to explore the potential for decoupled access/execute (DAE) software and hardware mechanisms. More specifically, we propose two software-only techniques, naive-software DAE and systolic-software DAE, along with a lightweight hardware access accelerator to further improve area-normalized throughput. We evaluate our techniques using a combination of PyTorch operator microbenchmarking and real-world PyTorch workloads running on a detailed register-transfer-level model of a 128-core manycore architecture. Our evaluation on three real-world dense and sparse tensor workloads suggest these workloads can achieve approximately 2-6× performance improvement when scaled to a future 2,000-core CPU-manycore heterogeneous system compared to an 18-core out-of-order CPU baseline, while potentially achieving higher area-normalized throughput and improved energyefficiency compared to general-purpose graphics processing units.

Dynamic and non-linear systems are emerging as potential candidates for random bit generation. In this context, chaotic systems, which are both dynamic and stochastic, are particularly suitable. This paper introduces a new continuous chaotic system along with its corresponding implementation, which targets field-programmable gate arrays (FPGAs). This chaotic system has five dimensions, which exhibit complex chaotic dynamics, thus enabling the utilization of chaotic signals in cryptography. A mathematical analysis is presented to demonstrate the dynamic characteristics of the proposed hyperchaotic system. A novel digital implementation of the proposed system is presented. Moreover, a data scrambling circuit is implemented to eliminate the bias effect and increase the randomness of the bitstream generated from the chaotic signals. We show that the proposed random bit generator has high randomness. The generated bits successfully pass well-known statistical randomness test-suites, i.e., NIST SP800-22, Diehard, and TestU01. The ready-to-use random bit generator is deployed on a Xilinx Zynq-7000 SoC ZC702 Evaluation Kit. Experimental results show that the proposed random bit generator can achieve a maximum throughput of 6.78 Gbps, which is over 3.6 times greater than state-of-the-art designs while requiring under 4% of the resources available on the targeted FPGA.