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This article proposes a simple method to calculate a current vector trajectory for the enhanced operation of the electric power generation system based on a synchronous reluctance machine (SynRM). Owing to magnetic saturation and cross magnetization, the performance and the torque capability of a SynRM vary according to the position and the value of the stator current. State-of-the-art control methods usually assume parameters with constant values, especially the inductance, neglecting saturation, leading to possible uncertainty in the machine operation. Therefore, a current vector trajectory to operate this type of machine, as a generator, in an extended speed range, with enhanced performance and considering magnetic saturation is proposed. A straightforward algorithm based on the inductance characteristic of the machine is used to calculate the trajectory of the stator current vector. This trajectory is evaluated via the numerical simulation of an experimentally validated finite-element model of a SynRM. The results show that the proposed current vector trajectory can improve the torque capability to 5% concerning the estimated trajectory without considering saturation. Experimental results are also provided to demonstrate the enhanced operation of the generator. [ABSTRACT FROM AUTHOR]

This article proposes an integrated regenerative braking energy utilization system (RBEUS) to improve regenerative braking energy (RBE) utilization in electrified railways. The proposed RBEUS uses a traction substation energy storage system and two sectioning post converters to achieve coordinated RBE utilization in three consecutive traction substations via power-sharing and storage, and the power quality can also be improved. A hierarchically coordinated control strategy is developed based on the operation principle to provide real-time power management and control for the RBEUS. In the system layer, a centralized power management strategy is designed for operation mode management and active power command generation. It uses a sequential quadratic programming-based algorithm to solve the objective function to achieve optimal RBE utilization under different operation modes. In the converter layer, local controllers of the RBEUS enable converters to respond to the active power commands from the system layer and reactive power commands generated by themself for power flow control. The effectiveness of the proposed RBEUS is comprehensively verified by using a hardware-in-the-loop experiment. Besides, a comparison analysis of the proposed RBEUS and literature methods is conducted to testify the superiority of the RBEUS. The feasibility of RBEUS implementation is also discussed from fault protection and economy. [ABSTRACT FROM AUTHOR]

This article presents a novel method for enabling energy-efficient and orientation-independent gravity compensation in active vibration isolation systems by taking advantage of the inherent negative stiffness of a hybrid reluctance actuator (HRA). Counteracting the gravitational force acting on the mover of an HRA with the position dependent force of the actuator’s integrated permanent magnet, the gravitational force can be compensated without the need for an additional actuation. An HRA-system with two translational degrees of freedom (DOF) is developed, comprising a permanent magnet for generating a constant biasing flux for both system axes and two actuator coils per axis for actively controlling the position of the mover. The system prototype has an actuation range of $\pm$ 0.7 mm in both DOF, while enabling energy efficient gravity compensation of payloads up to 500 g by an additional current feedback control loop. Experiments demonstrate that the current consumption for compensation of a 500 g payload is reduced from 1.58 A to 10 mA, which corresponds to a reduction of the power consumption by a factor of 25 000. [ABSTRACT FROM AUTHOR]

The safety and energy efficiency of autonomous vehicles in the vehicle-following driving scenario is seriously affected by the decision-making framework. This article proposes a safe and energy-saving decision-making framework for autonomous vehicles to avoid the potential rear-end collision and improve the energy efficiency in vehicle-following driving scenario. To realize this framework, the vehicle longitudinal dynamics model is first presented, and the autonomous vehicle's requirements from the safety and energy efficiency perspectives are comprehensively analyzed in the vehicle-following driving scenario. Then, a two-stage decision-making framework is designed. The first stage determines the safety region in the powertrain control domain, and the second stage determines the optimal energy efficiency in the safety region. Based on the two-stage decision-making framework, the perspective of “safety first” principle can be enforced by simultaneously considering the driving safety and energy efficiency. Finally, the experiment is conducted to demonstrate the advantages of the proposed method by comparing it with two other state-of-art methods. [ABSTRACT FROM AUTHOR]

Improving result accuracy in approximate computing (AC)-based real-time applications without violating deadlines has recently become an active research domain. Execution time of AC real-time tasks can individually be separated into: execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve the result accuracy of the initial result within a given deadline. However, obtaining higher result accuracy at the cost of enhanced execution time may lead to deadline violation, along with higher energy usage. We present ACCURATE, a novel hybrid offline–online approximate real-time scheduling approach that first schedules AC-based tasks on multicore with an objective to maximize result accuracy and determines operational processing speeds for each task constrained by system-wide power limit, deadline, and task dependency. At runtime, by employing a way-sharing technique (WH_LLC) at the last level cache (LLC), ACCURATE improves performance, which is further leveraged, to enhance result accuracy by executing more from the optional part and to improve the energy efficiency of the cache by turning off a controlled number of cache ways. ACCURATE also exploits the slacks either to improve the result accuracy of the tasks or to enhance the energy efficiency of the underlying system, or both. ACCURATE achieves 85% QoS with 36% average reduction in cache leakage consumption with a 24% average gain in energy-delay product (EDP) for a 4-core-based chip multiprocessor (CMP) with 6.4% average improvement in performance. [ABSTRACT FROM AUTHOR]

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of the hottest server components (i.e., cores and cache). Previous methods focused on either improving energy efficiency by adopting new hybrid-cache architectures including the resistive random-access memory (RRAM) and static random-access memory (SRAM) at the hardware level, or exploring tradeoffs between lifetime limitation and performance of multicore processors under stable workloads conditions. Therefore, no work has so far proposed a co-optimization method with hybrid-cache-based server architectures for real-life dynamic scenarios taking into account scalability, performance, lifetime reliability, and energy efficiency at the same time. In this article, we first formulate a reliability model for the hybrid-cache architecture to enable precise lifetime reliability management and energy efficiency optimization. We also include the performance and energy overheads of cache switching, and optimize the benefits of hybrid-cache usage for better energy efficiency and performance. Then, we propose a runtime $q$ -learning-based reliability management and performance optimization approach for multicore microprocessors with the hybrid-cache architecture, jointly incorporated with a dynamic preemptive priority queue management method to improve the overall tasks’ performance by targeting to respect their end time limits. Experimental results show that our proposed method achieves up to 44% average performance (i.e., tasks execution time) improvement, while maintaining the whole system design lifetime longer than five years, when compared to the latest state-of-the-art energy efficiency optimization and reliability management methods for computing servers. [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM) is a type of powerful deep neural network that has been widely used in many sequence analysis and modeling applications. However, the large model size problem of LSTM networks make their practical deployment still very challenging, especially for the video recognition tasks that require high-dimensional input data. Aiming to overcome this limitation and fully unlock the potentials of LSTM models, in this paper we propose to perform algorithm and hardware co-design towards high-performance energy-efficient LSTM networks. At algorithm level, we propose to develop fully decomposed hierarchical Tucker (FDHT) structure-based LSTM, namely FDHT-LSTM, which enjoys ultra-low model complexity while still achieving high accuracy. In order to fully reap such attractive algorithmic benefit, we further develop the corresponding customized hardware architecture to support the efficient execution of the proposed FDHT-LSTM model. With the delicate design of memory access scheme, the complicated matrix transformation can be efficiently supported by the underlying hardware without any access conflict in an on-the-fly way. Our evaluation results show that both the proposed ultra-compact FDHT-LSTM models and the corresponding hardware accelerator achieve very high performance. Compared with the state-of-the-art compressed LSTM models, FDHT-LSTM enjoys both order-of-magnitude reduction (more than $1000 \times$ 1000 × ) in model size and significant accuracy improvement (0.6% to 12.7%) across different video recognition datasets. Meanwhile, compared with the state-of-the-art tensor decomposed model-oriented hardware TIE, our proposed FDHT-LSTM architecture achieve $2.5\times$ 2. 5 × , $1.46\times$ 1. 46 × and $2.41\times$ 2. 41 × increase in throughput, area efficiency and energy efficiency, respectively on LSTM-Youtube workload. For LSTM-UCF workload, our proposed design also outperforms TIE with $1.9\times$ 1. 9 × higher throughput, $1.83\times$ 1. 83 × higher energy efficiency and comparable area efficiency. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) are growing in importance and complexity. An NN's performance (and energy efficiency) can be bound either by computation or memory resources. The processing-in-memory (PIM) paradigm, where computation is placed near or within memory arrays, is a viable solution to accelerate memory-bound NNs. However, PIM architectures vary in form, where different PIM approaches lead to different tradeoffs. Our goal is to analyze, discuss, and contrast dynamic random-access memory (DRAM)-based PIM architectures for NN performance and energy efficiency. To do so, we analyze three state-of-the-art PIM architectures: 1) UPMEM, which integrates processors and DRAM arrays into a single 2-D chip, 2) Mensa, a 3-D-stacking-based PIM architecture tailored for edge devices, and 3) SIMDRAM, which uses the analog principles of DRAM to execute bit-serial operations. Our analysis reveals that PIM greatly benefits memory-bound NNs: 1) UPMEM provides 23× the performance of a high-end graphics processing unit (GPU) when the GPU requires memory oversubscription for a general matrix–vector multiplication kernel, 2) Mensa improves energy efficiency and throughput by 3.0× and 3.1× over the baseline Edge tensor processing unit for 24 Google edge NN models, and 3) SIMDRAM outperforms a central processing unit/graphics processing unit by 16.7×/1.4× for three binary NNs. We conclude that the ideal PIM architecture for NN models depends on a model's distinct attributes, due to the inherent architectural design choices. [ABSTRACT FROM AUTHOR]

In this article, we propose an energy-efficient architecture, which is designed to receive both images and text inputs as a step toward designing reinforcement learning agents that can understand human language and act in real-world environments. We evaluate our proposed method on three different software environments and a low power drone named Crazyflie to navigate toward specified goals and avoid obstacles successfully. To find the most efficient language-guided reinforcement learning model, we implemented the model with various configurations of image input sizes and text instruction sizes on the Crazyflie drone GAP8, which consists of eight reduced instruction set computer-V cores. The task completion success rate and onboard power consumption, latency, and memory usage of GAP8 are measured and compared with Jetson TX2 ARM central processing unit and Raspberry Pi 4. The results show that by decreasing 20% of input image size we achieve up to 78% energy improvement while achieving an 82% task completion success rate. [ABSTRACT FROM AUTHOR]

At the energy consumption level, buildings are currently the largest sector, therefore, the energy efficiency of these structures, assumes a priority role in refurbishing projects. The “building automation” as well as the redesign of the electrical and thermal system of a structure can offer significant contributions in terms of energy saving, comfort, and functionality. Recent policies introduced the nearly zero-energy building (nZEB) qualification. The challenge we are facing is to work on the refurbishment of existing buildings by identifying a model that on the one hand allows nZEB qualification and on the other hand applies to existing buildings with all the technical and economic constraints. The goal of this article is to present possible redevelopment projects, thanks to the use of software, applicable to the historical university department located in the center of Rome to compare the parameters and reference indices for calculating the performance of an nZEB included in the ministerial decree (DM) June 26, 2015. The building automation and the redesign of the thermal electrical system of the structure is one of the most important aspects to obtain nZEB evaluation but it is also important to change opaque vertical and horizontal structures and transparent envelopes. [ABSTRACT FROM AUTHOR]

Massive multiple access schemes, capable of serving a large number of uncoordinated devices while fulfilling reliability and latency constraints, are proposed. The schemes belong to the class of grant-free coded random access protocols and are tailored to massive multiple input multiple output (MIMO) base station processing. High reliability is obtained owing to an intra-frame spatial coupling effect, triggered by a simple device access protocol combined with acknowledgements (ACKs) from the base station. To provide system design guidelines, analytical bounds on error floor and latency are also derived. The proposed schemes are particularly interesting to address the challenges of massive machine-type communications in the framework of next generation massive multiple access systems. [ABSTRACT FROM AUTHOR]

Multiple photovoltaic (PV) modules are often used to provide enhanced harvesting capability for light energy-based Internet of Things (IoT) devices. PV modules facing multiple directions can lead to a situational energy loss when parts of the modules are shaded. To address this issue, existing solutions exploit reconfigurable PV arrays to acquire the optimal configuration in a given situation. However, conventional techniques are not energy efficient in estimating the harvesting capability of PV modules, and require high computation to find the optimal PV array at runtime. In this article, we propose PVoT, an energy-efficient reconfigurable PV array, which maximizes the harvesting energy for indoor IoT devices. To this end, we propose the use of photoresistors to estimate the harvesting capability with minimal energy overhead. We also provide hardware and software schemes, which perform event-driven light change detection in an energy-efficient way. Furthermore, we develop a power imbalance threshold metric to quickly find the optimal PV array at runtime. We implemented a prototype PVoT with off-the-shelf components and accompanying software. Experiments with the prototype hardware showed that PVoT achieves a gain of up to 23.9% in harvested energy compared to the existing directly connected PV array scheme. [ABSTRACT FROM AUTHOR]

Processing-in-memory (PIM) is a promising technique to accelerate deep learning (DL) workloads. Emerging DL workloads (e.g., ResNet with 152 layers) consist of millions of parameters, which increase the area and fabrication cost of monolithic PIM accelerators. The fabrication cost challenge can be addressed by 2.5-D systems integrating multiple PIM chiplets connected through a network-on-package (NoP). However, server-scale scenarios simultaneously execute multiple compute-heavy DL workloads, leading to significant interchiplet data volume. State-of-the-art NoP architectures proposed in the literature do not consider the nature of DL workloads. In this article, we propose a novel server scale 2.5-D manycore architecture called SWAP that accounts for the traffic characteristics of DL applications. Comprehensive experimental evaluations with different system sizes as well as diverse emerging DL workloads demonstrate that SWAP achieves significant performance and energy consumption improvements with much lower fabrication cost than state-of-the-art NoP topologies. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are being heavily utilized in modern applications, putting energy-constraint devices to the test. To bypass high energy consumption issues, approximate computing has been employed in DNN accelerators to balance out the accuracy-energy reduction trade-off. However, the approximation-induced accuracy loss can be very high and drastically degrade the performance of the DNN. Therefore, there is a need for a fine-grain mechanism that would assign specific DNN operations to approximation to maintain acceptable DNN accuracy, while achieving low energy consumption. We present an automated framework for weight-to-approximation mapping through formal property exploration for approximate DNN accelerators. At the MAC unit level, our experimental evaluation surpassed already energy-efficient mappings by more than $\times 2$ in terms of energy gains, while supporting a fine-grain control over the introduced approximation. [ABSTRACT FROM AUTHOR]

Photonic microring resonator (MRR)-based hardware accelerators have been shown to provide disruptive speedup and energy-efficiency improvements for processing deep convolutional neural networks (CNNs). However, previous MRR-based CNN accelerators fail to provide efficient adaptability for CNNs with mixed-sized tensors. One example of such CNNs is depthwise separable CNNs. Performing inferences of CNNs with mixed-sized tensors on such inflexible accelerators often leads to low hardware utilization, which diminishes the achievable performance and energy efficiency from the accelerators. In this article, we present a novel way of introducing reconfigurability in the MRR-based CNN accelerators, to enable dynamic maximization of the size compatibility between the accelerator hardware components and the CNN tensors that are processed using the hardware components. We classify the state-of-the-art MRR-based CNN accelerators from prior works into two categories, based on the layout and relative placements of the utilized hardware components in the accelerators. We then use our method to introduce reconfigurability in accelerators from these two classes, to consequently improve their parallelism, the flexibility of efficiently mapping tensors of different sizes, speed, and overall energy efficiency. We evaluate our reconfigurable accelerators against three prior works for the area proportionate outlook (equal hardware area for all accelerators). Our evaluation for the inference of four modern CNNs indicates that our designed reconfigurable CNN accelerators provide improvements of up to $1.8\times $ in frames-per-second (FPS) and up to $1.5\times $ in FPS/W, compared to an MRR-based accelerator from prior work. [ABSTRACT FROM AUTHOR]

Stochastic computing (SC) has recently emerged as a promising method for efficient machine learning acceleration. Its high compute density, affinity with dense linear algebra primitives, and approximation properties have an uncanny level of synergy with the deep neural network computational requirements. However, there is a conspicuous lack of works trying to integrate SC hardware with sparsity awareness, which has brought significant performance improvements to conventional architectures. In this work, we identify why common sparsity-exploiting techniques are not easily applicable to SC accelerators and propose a new architecture—SASCHA—sparsity-aware SC hardware architecture for the neural network acceleration that addresses those issues. SASCHA encompasses a set of techniques that make utilizing sparsity in inference practical for different types of SC computation. At 90% weight sparsity, SASCHA can be up to $6.5\times $ faster and $5.5\times $ more energy-efficient than comparable dense SC accelerators with a similar area without sacrificing the dense network throughput. SASCHA also outperforms sparse fixed-point accelerators by up to $4\times $ in terms of latency. To the best of our knowledge, SASCHA is the first SC accelerator architecture oriented around sparsity. [ABSTRACT FROM AUTHOR]

Mobile edge computing is popular for providing services with low latency and high privacy. It becomes more powerful by leveraging the widely deployed Small cell Base Stations (SBS) with zooming cells for energy-efficient service placement. However, the literature ignores the joint optimization of SBS power control and service placement. The Quality of Services (QoS) guarantee is also challenging, taking into account the component dependency and parallelization in multi-component services and the resource slicing for separated service deployment. Moreover, prior works assume the same amount of allocated resources of links on a placed path for service edge allocation, leading to the ossification of resource allocation and inevitable resource fragments. Towards the above challenges, this paper addresses the energy-efficient and flexible service placement in cell zooming enabled Mobile Edge Cloud (MEC) networks. The delay of multi-component services is constructed depending on the allocated resource slice. Besides, the joint optimization of service placement and SBS power control is formulated and transformed into a Mixed Integer Linear Programming (MILP). More importantly, the equivalent bandwidth for an edge allocation is defined and analyzed to obtain flexible edge placement with minimum resource cost. Leveraging the results of MILP, an Energy-efficient Service placement algorithm based on equivalent Bandwidth in Cell zooming enabled MEC networks (ESBC) is proposed to improve the probability of successful service placement with QoS guarantee by optimizing the delay distribution among components and edges and reducing resource fragments. Finally, simulation results validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Indoor multi-robot communications face two key challenges: one is the severe signal strength degradation caused by blockages (e.g., walls) and the other is the dynamic environment caused by robot mobility. To address these issues, we consider the reconfigurable intelligent surface (RIS) to overcome the signal blockage and assist the trajectory design among multiple robots. Meanwhile, the non-orthogonal multiple access (NOMA) is adopted to cope with the scarcity of spectrum and enhance the connectivity of robots. Considering the limited battery capacity of robots, we aim to maximize the energy efficiency by jointly optimizing the transmit power of the access point (AP), the phase shifts of the RIS, and the trajectory of robots. A novel federated deep reinforcement learning (F-DRL) approach is developed to solve this challenging problem with one dynamic long-term objective. Through each robot planning its path and downlink power, the AP only needs to determine the phase shifts of the RIS, which can significantly save the computation overhead due to the reduced training dimension. Simulation results reveal the following findings: i) the proposed F-DRL can reduce at least 86% convergence time compared to the centralized DRL; ii) the designed algorithm can adapt to the increasing number of robots; iii) compared to traditional OMA-based benchmarks, NOMA-enhanced schemes can achieve higher energy efficiency. [ABSTRACT FROM AUTHOR]

Data centers are facilities with large number of servers providing cloud services. The increasing number of data centers in the last years has generated environmental concern due to the large amount of energy consumed by them. This also includes some auxiliary services such as the cooling equipment which is known to be very costly. For that reason, efficient data center strategies are needed in order to provide an acceptable quality of service (QoS) and suitable temperature for every server while using the least amount of resources possible. This article presents some strategies to deal with the unified workload and temperature problem that appears in the data center. As the system is modeled as a queue and the control variables have an hybrid nature, some highly parallelizable particle-based optimization algorithms are proposed to solve the optimization problem. Numerical simulations are provided in order to illustrate the effectiveness of the strategy. These simulations also show the improvements obtained from the GPU computing. Finally, a probabilistic evaluation approach is developed in order to provide certificates on the probability of constraint satisfaction without increasing the computational burden of the online problem. Note to Practitioners—This article addresses the problem of deciding in real-time the number of active servers in a data center that is required to meet the quality of service (QoS) demands while keeping energy consumption at a minimum. The temperature set point of the cooling equipment must also be taken into account, as it is advisable to use the minimum cooling that keeps the servers running in safe conditions. The management strategy proposed is based on predictive control. In this way, the number of active servers and temperature set point will be chosen so that the future energy consumption is minimized while guaranteeing that QoS and safety demands are met under different possible operating conditions. Furthermore, the proposed management strategy can be tuned depending on the QoS that it is desirable to provide. The proposed strategy will lead to energy-consumption improvements while having guarantees on the data center performance. [ABSTRACT FROM AUTHOR]

Approximate computing is a promising alternative to improve energy efficiency for IoT devices on the edge. This work proposes a piecewise-linearly-approximated and unbiased floating-point approximate multiplier with run-time configurability. We provide a theoretically sound formulation that turns multiplication approximation to an optimization problem. With the formulation and findings, a multi-level architecture is proposed to easily incorporate run-time configurability and module execution parallelism. Finally, the proposed multiplier is further optimized to reduce the circuit implementation complexity, making the multiplier linearly dependent on the precision requirement, instead of quadratically or exponentially as in prior work. When compared to the prior state-of-the-art approximate floating-point multiplier, ApproxLP M. Imani et al, “ApproxLP: Approximate multiplication with linearization and iterative error control,” in Proc. ACM/IEEE Des. Autom. Conf., 2019, pp. 1–6., the proposed multiplier outperforms in all the aspects including accuracy, area, and delay. By replacing a full-precision floating-point multiplier in GPU, the proposed design can improve the energy efficiency for various edge computing tasks. Even with Level 1 approximation, the proposed multiplier improves energy efficiency up to 20× for machine learning on CIFAR-10, with almost negligible accuracy loss. [ABSTRACT FROM AUTHOR]

This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users’ rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs. [ABSTRACT FROM AUTHOR]

Energy-efficient time synchronizations for wireless sensor networks (WSNs) have been put under the spotlight for years. A promising technique among which is the timestamp-free approach where no timestamps are required to establish the synchronization, thereby sparing the transmissions of the timing messages for conserving significant transmission energy. In this paper, we first investigate the feasibility of adopting timestamp-free time synchronization in practical resource-constrained WSNs; we then identify the issue of inaccuracy in maintaining the pre-defined response interval which affects the foundations of most existing timestamp-free schemes. Based on the investigation and our previously proposed reverse asymmetric time synchronization framework, we further propose an asymmetric timestamp-free time synchronization scheme with two estimation methods tailored for resource-constrained WSNs. We as well introduce the centralized and distributed multi-hop extension methods for the proposed scheme to cover diverse multi-hop scenarios. Experimental results on a real WSN testbed consisting of TelosB motes running TinyOS demonstrate that the proposed scheme achieves high energy efficiency while maintaining microsecond-level time synchronization accuracy compared to three other conventional schemes. [ABSTRACT FROM AUTHOR]

Demand for coarse grain reconfigurable architectures (CGRAs) has significantly increased in recent years as architectures need to be both energy efficient and flexible. However, most CGRAs are optimized for performance instead of energy efficiency. In this work, a novel paradigm for reconfigurable architectures, Blocks, is presented. Blocks uses two separate circuit-switched networks, one for control and one for the data path. This enables the runtime construction of energy-efficient application-specific VLIW-SIMD processors on a reconfigurable fabric. Its energy efficiency is demonstrated by comparing Blocks to four reference architectures, a VLIW, an SIMD, a commercial low-power microprocessor, and a traditional CGRA. All comparisons are based on commercial low-power 40-nm CMOS layout, including memories. Results show that Blocks can achieve a mean total energy reduction of $2.05\times $ , $1.84\times $ , $8.01\times $ , and $1.22\times $ over a VLIW, an SIMD, an energy-efficient microprocessor and a traditional CGRA, respectively. At the same time, Blocks delivers equal or higher performance per area due to its ability to adapt to applications by reconfiguration. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have become the driving force behind recent artificial intelligence (AI) research. With the help of a vast amount of training data, neural networks can perform better than traditional machine learning algorithms in many applications. An important problem with implementing a neural network is the design of its architecture. Typically, such an architecture is obtained manually by exploring its hyperparameter space and kept fixed during training. This approach is both time consuming and inefficient. Another issue is that modern neural networks often contain millions of parameters, whereas many applications require small inference models due to imposed resource constraints, such as energy constraints on battery-operated devices. However, efforts to migrate DNNs to such devices typically entail a significant loss of classification accuracy. To address these challenges, we propose a two-step neural network synthesis methodology, called DR+SCANN, that combines two complementary approaches to design compact and accurate DNNs. At the core of our framework is the SCANN methodology that uses three basic architecture-changing operations, namely, connection growth, neuron growth, and connection pruning, to synthesize feedforward architectures with arbitrary structure. These neural networks are not limited to the multilayer perceptron structure. SCANN encapsulates three synthesis methodologies that apply a repeated grow-and-prune paradigm to three architectural starting points. DR+SCANN combines the SCANN methodology with dataset dimensionality reduction to alleviate the curse of dimensionality. We demonstrate the efficacy of SCANN and DR+SCANN on various image and nonimage datasets. We evaluate SCANN on MNIST, CIFAR-10, and ImageNet benchmarks. Without any loss in accuracy, SCANN generates a $46.3\times $ smaller network than the LeNet-5 Caffe model. We also compare SCANN-synthesized networks with a state-of-the-art fully connected (FC) feedforward model for MNIST, and show $20\times $ ($19.9\times $) reduction in the number of parameters (floating-point operations) with little drop in accuracy. For the CIFAR-10 dataset, we target AlexNet and VGG-16 baseline architectures. SCANN reduces the number of parameters in AlexNet by $10.1\times $ without any drop in accuracy. It reduces the number of parameters in the FC layers of VGG-16 to only 2.5k while increasing accuracy by 1.05%. On the ImageNet dataset, for the VGG-16 and MobileNetV2 architectures, we reduce network parameters by $8.0\times $ and $1.3\times $ , respectively, with a similar or improved performance over their respective baselines. We also evaluate the efficacy of using dimensionality reduction alongside SCANN (DR+SCANN) on nine small-to-medium-size datasets. Using this methodology enables us to reduce the number of connections in the network by up to $5078.7\times $ (geometric mean: $82.1\times $), with little to no drop in accuracy. On seven out of nine datasets, we show 0.41%–10.09% accuracy improvements over the FC baseline models. We also show that our synthesis methodology yields neural networks that are much better at navigating the accuracy versus energy efficiency space. This can enable neural network-based inference even on Internet-of-Things sensors. [ABSTRACT FROM AUTHOR]

As performance and energy-efficiency improvements from technology scaling are slowing down, new technologies are being researched in hopes of disrupting results. Domain wall memory (DWM) is an emerging non-volatile technology that promises extreme data density, fast access times and low power consumption. However, DWM access time depends on the memory location distance from access ports, requiring expensive shifting. This causes overheads on performance and energy consumption. In this article, we implement our previously proposed shift-reducing instruction memory placement (SHRIMP) on a RISC-V core in RTL, provide the first thorough evaluation of the control logic required for DWM and SHRIMP and evaluate the effects on system energy and energy-efficiency. SHRIMP reduces the number of shifts by 36% on average compared to a linear placement in CHStone and Coremark benchmark suites when evaluated on the RISC-V processor system. The reduced shift amount leads to an average reduction of 14% in cycle counts compared to the linear placement. When compared to an SRAM-based system, although increasing memory usage by 26%, DWM with SHRIMP allows a 73% reduction in memory energy and 42% relative energy delay product. We estimate overall energy reductions of 14%, 15% and 19% in three example embedded systems. [ABSTRACT FROM AUTHOR]

Machine-type communications (MTC) is a key technology for Internet-of-Things (IoT) services in 5G mobile communications and beyond. An essential design problem for an MTC network is the efficient and scalable data collection from low-power machine-type communication devices (MTCDs). This paper uses unmanned aerial vehicles (UAVs) to facilitate data collection from a clustered MTC network on the ground. The notion of artificial energy map (AEM) is introduced as a novel modeling technique for energy efficiency analysis, which is critical to the subject of investigation here considering the limited energy of battery-powered MTCDs and UAVs. The proposed design framework first determines the number of MTCD clusters $L_\mathrm{{opt}}$ according to a certain criterion. A greedy learning clustering (GLC) algorithm is then employed to divide the MTCDs into $L_\mathrm{{opt}}$ clusters. For each MTCD cluster, an AEM is constructed, and the optimal UAV hovering strategy within the cluster can be obtained accordingly. Finally, the UAV stations travel across the clusters and collect data from each cluster while hovering above it. This AEM-based modeling technique leads to a solution that can effectively improve the energy efficiency (EE) of UAV-enabled data collection. However, the MTCD clustering strategy, UAV hovering strategy, and UAV flying strategy all have impacts on the overall energy efficiency, which results in a coupled optimization problem that is difficult to solve. The GLC-AEM method is proposed to decouple the original EE optimization problem into sub-problems that can be handled easily by standard optimization techniques. Simulation results show that the GLC-AEM algorithm can be applied to UAV-enabled data collection scenarios with single and multiple UAV stations, and it can improve the overall EE effectively. Besides, the GLC-AEM algorithm shows good scalability and consistent performance in clustered MTC networks. The more MTCDs, the higher the achieved EE. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has been introduced as a promising emerging paradigm to design a smart radio environment for improving wireless capacity. It has the potential to reshape wave propagation via its tunable and low-cost reflecting elements. Therefore, it is regarded as a propitious solution that provides alternative paths for the propagation of high-frequency signals, which are vulnerable to blockages and suffer from penetration losses. Because of those features, RIS has been considered to be integrated into the wireless vehicular network (WVN), which is characterized by high mobility and high quality of service (QoS) requirements. Hence, similar to relaying systems, a vehicle needs to select the best available RIS that maximizes the signal-to-noise ratio (SNR) at the receiver. Inspired by that, in this paper, we introduce the partial RIS selection for enhancing capacity and reducing error rate. We derive closed-form expressions for the ergodic capacity, symbol error probability (SEP), and energy efficiency for a partial RIS selection design. Besides, we compare its performances to the partial relaying design while considering: decode-and-forward (DF) and amplify-and-forward fixed gain (AF). Moreover, we consider a wireless attack scenario, where an eavesdropper intends to overhear the transmitted signal at the receiver’s end. Then, we compare partial RIS to partial relay selection from this security perspective, where we derive closed-form expressions for the average secrecy capacity. Furthermore, we compare the partial RIS selection to a single RIS architecture scenario from the literature, where our solution outperforms the related existing work. [ABSTRACT FROM AUTHOR]

Motor drives with interior permanent magnet synchronous motors are typically used with vector or rectangular control (RC). Both of these control methods have well-known pros and cons; therefore, the most appropriate control mode is selected at the stage of drive design. Then, at the experimental verification of the drive prototype, the optimal pulse-width modulation (PWM) frequency is detected and the operation of motor drive is adjusted to this frequency and the corresponding sampling time. The systems constructed according to this algorithm have efficiency with one peak, which is typically selected to be at the rated mode motor drive. As a result, motor drives operate in other modes with lower efficiency, which is subject to further improvements. This article proposes the usage of multicontrol mode, which combines the benefits from each control method. The author suggests usage of the RC at lower speeds and vector control at higher speeds. Furthermore, the PWM frequency of motor drives is also changed, depending on the operating speed, which improves drive performance. This combination of control methods results in efficiency, which has several maximums and increases overall drive efficiency. [ABSTRACT FROM AUTHOR]

The massive multiple-input multiple-output (MIMO) transmission technology has recently attracted much attention in the non-geostationary, e.g., low earth orbit (LEO) satellite communication (SATCOM) systems since it can significantly improve the energy efficiency (EE) and spectral efficiency. In this work, we develop a hybrid analog/digital precoding technique in the massive MIMO LEO SATCOM downlink, which reduces the onboard hardware complexity and power consumption. In the proposed scheme, the analog precoder is implemented via a more practical twin-resolution phase shifting (TRPS) network to make a meticulous tradeoff between the power consumption and array gain. In addition, we consider and study the impact of the distortion effect of the nonlinear power amplifiers (NPAs) in the system design. By jointly considering all the above factors, we propose an efficient algorithmic approach for the TRPS-based hybrid precoding problem with NPAs. Numerical results show the EE gains considering the nonlinear distortion and the performance superiority of the proposed TRPS-based hybrid precoding scheme over the baselines. [ABSTRACT FROM AUTHOR]

We consider the downlink of a relay-aided multi-cell massive multi-input multi-output (mMIMO) system, where in each cell the base station (BS) serves its users via multiple relays by employing non-orthogonal multiple access (NOMA). We model this system by considering spatially-correlated Rician-faded channels and their estimation errors. The users, consequently, perform imperfect successive interference cancellation (SIC). We derive a lower bound on the spectral efficiency (SE) of this system. We then optimize the non-convex global energy efficiency (GEE) metric, which is a fractional function of the optimization variables. We solve this problem by considering a low-complexity alternating minimization maximization approach, which splits a complex joint problem into multiple simpler convex surrogate sub-problems. We propose a novel surrogate function to exploit this framework, and analytically show that it satisfies the desirable properties of a valid surrogate function. We numerically show that 1) reusing the pilots in each cell, when the channel has sufficiently hardened, provides higher SE than using orthogonal pilots in all cells and 2) the proposed GEE algorithm provides similar GEE as that of an existing joint optimization framework, but with much less complexity. [ABSTRACT FROM AUTHOR]

A review of advanced Fe-based amorphous and nanocrystalline soft magnetic alloys that are currently being used in the market and their primary industrial applications is discussed as well as emerging trends. The high-efficiency distribution transformer market continues to be the largest segment by volume for Fe-based amorphous foil. The industry has been shifting toward higher induction levels (1.63 T) with increased stacking factor (>90%) to minimize the transformer size and reduce costs. New magnetic domain refined grades of amorphous foil are now commercially available which show a reduction of ~25% in core losses compared to the standard amorphous material. Nanocrystalline soft magnetic alloys have become the standard material for many applications including low-loss electrical components, magnetic shielding, electronic filtering, medium frequency transformer cores, and a variety of others. The nanocrystalline alloys are commonly cast fully amorphous and devitrified through controlled heat treatments. New nanocrystalline alloy compositions and processing methods have enhanced both the mechanical and magnetic performance of the final properties. Amorphous and nanocrystalline alloys show promise for use in electric motors but fabrication challenges for limited their commercial adoption. [ABSTRACT FROM AUTHOR]

In Turkey, refugees from Syria have been continuously increasing with rapid urbanization. Concurrently, energy consumption has been growing promptly. Supply and demand equality may be a crucial problem in the electricity market causing further instability and shortages leading to reliable predictions a necessity. For this reason, an informative questionnaire is prepared to take the expert opinions on the main drivers that shape the energy consumption behavior of Syrian refugees. In total, 17 experts are interviewed. The answers are explored by using NETICA software for Bayesian Belief Networks. Factors affecting energy consumption behavior and also strategies, institutions, tools, and financing methods that change the behavior toward efficient consumption are investigated. Based on obtained results, it can be claimed that to be successful, electricity prices and tariffs in the market should be arranged in the favor of energy efficiency. Besides, support mechanisms by not only the government but also by the municipalities should be considered while setting related policies. Also, electric appliance producers should develop and implement strategies and actions to encourage the usage of more efficient appliances. Last but not the least, nongovernmental organizations should raise refugees’ awareness of the efficient electricity consumption to minimize the impacts of refugee inflows on the electricity market. [ABSTRACT FROM AUTHOR]

This paper studiesan unmanned aerial vehicle (UAV)-assisted wireless network, where a UAV is dispatched to gather information from ground sensor nodes (SN) and transfer the collected data to a depot. The information freshness is captured by the age of information (AoI) metric, whilst the energy consumption of the UAV is seen as another performance criterion. Most importantly, the AoI and energy efficiency are inherently competing metrics, since decreasing the AoI requires the UAV returning to the depot more frequently, leading to a higher energy consumption. To this end, we design UAV paths that optimize these two competing metrics jointly and reveal the Pareto frontier. To formulate this problem, a multi-objective mixed integer linear programming (MILP) is proposed with a flow-based constraint set and we apply Bender’s decomposition on the proposed formulation. Numerical results show that the proposed method allows deriving non-dominated solutions among two competing metrics when designing the UAV path. [ABSTRACT FROM AUTHOR]

In this paper, we consider an energy efficient resource allocation technique for a hybrid time division multiple access (TDMA) - non-orthogonal multiple access (NOMA) system. In such a hybrid system, the available time for transmission is divided into several sub-time slots, and a sub-time slot is allocated to serve a group of users (i.e., cluster). Furthermore, signals for the users in each cluster are transmitted based on the NOMA approach. With NOMA, multiple users can be served simultaneously through utilizing power domain multiplexing at transmitter and successive interference cancellation (SIC) at receiver. In this paper, to maximize the energy efficiency (EE), we jointly allocate both the available time slots and the available transmit power in the hybrid TDMA-NOMA system. In particular, we formulate an EE maximization (EE-Max) problem aiming to maximize the overall EE of the system with a per-user minimum rate and transmit power constraints. However, this joint optimization problem is non-convex in nature, and thus, cannot be solved directly. Therefore, we develop an iterative algorithm by approximating the original problem into a convex one with sequential convex approximation (SCA) and a novel second-order cone (SOC) approach. Simulation results demonstrate that the performance of the proposed hybrid TDMA-NOMA system with joint resource allocation outperforms the system with equal time allocation in terms of the overall EE. Simulation results further confirm that the proposed iterative approaches with SCA and SOC techniques converge within a few number of iterations while yielding the solution to the original non-convex problem. [ABSTRACT FROM AUTHOR]

Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This article brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing, thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. This work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We, therefore, outline future directions for minimizing the environmental impact of computing systems. [ABSTRACT FROM AUTHOR]

As a promising technology with disruptive innovation, user-centric network can meet the data traffic demand and user service demand in the future mobile networks. It completely transforms the traditional cell-centric paradigm into user-centric paradigm, requiring the deployment of a large number of access points (APs). However, the massive deployment of APs leads to issues such as high hardware cost, huge power consumption and complex interference management. Reconfigurable intelligent surfaces (RIS), as another promising technology, can expand signal coverage, suppress interference, reduce hardware cost and power consumption. Inspired by this, we propose a novel RIS assisted user-centric network that uses RIS to replace some low utilization APs to reduce cost as well as power consumption, and deploy more RIS to achieve energy-efficient user-centric communication. In order to take full advantages of RIS, we jointly optimize passive beamforming at RIS and power control at AP to maximize the energy efficiency of the network. Since this problem is intractable and non-convex, an effectively alternating optimization algorithm, capitalizing on fractional programming and successive lower-bound maximization is proposed. The simulation results verify that the proposed algorithm outperforms reference algorithms in terms of energy efficiency and sum rate. [ABSTRACT FROM AUTHOR]

IP traffic has increased significantly in recent years, and it is expected that this progress will continue. Recent studies report that the viewing of online video content accounts for a share of 1% of the global greenhouse gas emissions. To reduce the data traffic of video streaming, the new standard Versatile Video Coding (VVC) has been finalized in 2020. In this paper, the energy efficiency of two different VVC decoders is analyzed in detail. Furthermore, we propose a design space exploration that uses an algorithm based on a greedy strategy to derive coding tool profiles that optimize the energy demand of the decoder. We show that the algorithm derives optimal coding tool profiles for a subset of coding tools. Additionally, we propose profiles that reduce the energy demand of VVC decoders and provide energy savings of more than 50% for sequences with 4K resolution. Thereby, we will also show that the proposed profiles can have a lower decoding energy demand than comparable HEVC-encoded bit streams while also having a significantly lower bit rate. [ABSTRACT FROM AUTHOR]

Although it has been surpassed by many subsequent coding standards, JPEG occupies a large storage share of the current data hosting service. To reduce the storage costs, DropBox proposed a secondary lossless compression algorithm, Lepton, to further improve the compression rate of JPEG images. However, the bloated probability models defined by Lepton severely restrict its throughput and energy efficiency. To solve this problem, we construct an access probability-based hash function for the probability models, and then propose a hardware-friendly memory optimization method by combining the proposed hash function with N-way Set-Associative unit. Besides, we also propose a synchronization mechanism for the serial accessing probability model, so that the syntax elements can be processed in parallel without changing the resulting bitstream. After that, we implement a high throughput, high energy efficiency, and low-cost Lepton hardware encoder. To the best of our knowledge, this is the first hardware implementation of transparent image recompression. The synthesis result shows that the proposed hardware structure reduces the total area of the probability models by 70.97%. Compared with DropBox’s software solution, the throughput and the energy efficiency of the proposed Lepton hardware encoder are increased by 63 and 1398 times on average. In terms of manufacturing cost, the proposed Lepton hardware encoder is also lower than the general-purpose CPU used by DropBox. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) are widely used in general-purpose high-performance computing applications (i.e., GPGPUs), which require reliable execution in the presence of soft errors. To support massive thread-level parallelism, a sizeable register file is adopted in GPUs, which is highly vulnerable to soft errors. Although modern commercial GPUs provide single-error-correction double-error-detection (SEC-DED) error correction code (ECC) for the register file, it consumes a considerable amount of energy due to frequent register accesses and leakage power of ECC storage. In this article, we propose to leverage the error sensitivity of instructions, the duplicate characteristics of the same-named registers, and the error sensitivity of data bits to build a unified energy-efficient ECC mechanism for a GPGPUs register file (Eff-ECC), which consists of instruction-aware ECC (IA-ECC), duplication-aware ECC (DA-ECC), and bit-aware ECC (BA-ECC). Considering the error sensitivity of instructions, IA-ECC merely implements ECCs for the write registers of critical instructions. Observing the same-named registers across threads usually keeps the same data, DA-ECC avoids unnecessary ECC generation and verification for duplicate register values. Leveraging the inherent error-tolerance features of the program, BA-ECC merely protects significant bits of registers to combat the crucial error. Experimental results demonstrate that Eff-ECC tremendously reduces 86.46% energy consumption of traditional SEC-DED ECC. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient resource allocation problem in device-to-device (D2D) communications underlaying unmanned aerial vehicle (UAV)-enabled networks. The UAV is deployed as a flying base station to communicate with wireless users in the presence of an eavesdropper in the cell. We consider two types of users: the ground users (GUs) served by the UAV and the D2D users that communicate directly with one another. Our aim is to maximize the total energy efficiency (TEE) of all D2D pairs while guaranteeing the quality of service (QoS) requirements and secrecy rates of all GUs and D2D users via joint power control and channel allocation. The considered TEE maximization problem is a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve. Therefore, we propose a method that consists of outer and inner loops. In the outer loop, Dinkelbach's algorithm is utilized to transform the original fractional programming problem into a subtractive form. In the inner loop, we employ the alternating optimization method and divide the equivalent optimization problem into two sub-problems: power allocation and channel allocation. Solving the two sub-problems directly using standard convex optimization software may have high complexity. Therefore, we also propose a low-complexity algorithm using the Lagrangian dual and Kuhn—Munkres algorithm to obtain the optimal power allocation in closed-form and the optimal channel allocation, respectively. Simulation results show that the proposed algorithm converges in a small number of iterations. Furthermore, the proposed approach shows its superior performance compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

A secure energy efficient approach is proposed to connect Internet of Things (IoT) sensors that operate with limited power resources. This is done by optimizing simultaneously the energy efficiency, the communication rate and the network security while limiting the potential data leakage and tracking the finite battery status evolution. The proposed model uses spatial diversity in addition to artificial jamming introduced by an intermediate device to forward the data from the sensors to the destination and to secure the communication links without draining the rechargeable batteries. The energy harvested by the source is also maximized without affecting the security level of the network. The outage secrecy capacity is derived to evaluate the security level. Furthermore, the system power stability is analyzed using Markov chains and statistical approaches to validate the efficiency of the proposed technique in maintaining the system in a self-sufficient mode and making it operate without the assistance of external power resources. [ABSTRACT FROM AUTHOR]

We consider a two-way full-duplex (FD) system where a massive multi-input-multi-output FD base station (BS) communicates with multiple FD users via an intelligent reflecting surface (IRS). We derive a closed-form network spectral efficiency lower bound by considering spatial correlation at the BS and IRS. We demonstrate the importance of modelling spatial correlation by simplifying this lower bound for uncorrelated channels to show that the IRS capability to modify the wireless medium now is significantly impeded. This lower bound, which is a function of only the long-term channel statistics, is also used to maximize the non-concave global energy efficiency metric by optimally allocating the transmit powers, and by designing the IRS phases. We derive closed-form updates for optimizing the transmit powers using Lagrangian dual, Quadratic, and Dinkelbach’s transforms. We then optimize the IRS phases by using the projected gradient ascent algorithm. We numerically show that increasing the number of IRS elements can help a FD mMIMO BS outperform its half-duplex counterpart, which otherwise under-performs due to its limited ability to cancel various FD interferences. [ABSTRACT FROM AUTHOR]

This article introduces a semi-analytical method of predicting ac loss in commercial Litz wires. Simple finite-element simulations are used to compute the resultant proximity fields in a coil system of arbitrary geometry. The approach addresses non-ideal Litz wire construction by applying a surrogate skin effect model to inform and distribute the current over the cross Section into segmented layers. This simplifies the finite-element problem into a 2-D, dc simulation with a low number of mesh elements. Analytical solutions are then used to compute frequency-dependent loss due to skin and proximity effect. The method is demonstrated using two 14 American Wire Gauge (AWG) equivalent Litz wires with very different constructions and is validated with experimental results from several coil configurations. Finally, an appeal is made to commercial Litz wire manufacturers to provide an empirical “fabrication factor” specification that would allow consumers to predict the performance of a conductor in their application. [ABSTRACT FROM AUTHOR]

This paper proposes a traction unit that combines an interior permanent magnet motor and a PM-assisted synchronous reluctance motor for electric vehicles to improve traction performance, enhance energy efficiency, and reduce cost. The characteristics of three traction units are analyzed and compared, including (a) rear-wheel-drive configuration using one IPM motor; (b) all-wheel-drive configuration using two interior permanent magnet motors; and (c) all-wheel-drive with a combination of one interior permanent magnet motor and one PM-assisted synchronous reluctance as proposed in this paper. The operational characteristics of each motor to achieve a wide constant power speed range are first investigated. Then, the Urban Dynamometer Driving Schedule is utilized to compare and evaluate the operational characteristics, energy consumption, and most importantly, the efficiency of the traction units. Simulation results reveal that the overall efficiency of the proposed traction unit can be improved compared to the other configurations. The advantages and drawbacks of the proposed traction unit are also investigated. Finally, experiments are conducted to validate the analysis. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) over wireless network enables data-conscious services by leveraging the ubiquitous intelligence at network edge for privacy-preserving model training. As the proliferation of context-aware services, the diversified personal preferences cause disagreeing conditional distributions among user data, which leads to poor inference performance. In this sense, clustered federated learning is proposed to group user devices with similar preference and provide each cluster with a personalized model. This calls for innovative design in edge association that involves user clustering and also resource management optimization. We formulate an accuracy-cost trade-off optimization problem by jointly considering model accuracy, communication resource allocation and energy consumption. To comply with parameter encryption techniques in FL, we propose an iterative solution procedure which employs deep reinforcement learning based approach at cloud server for edge association. The reward function consists of minimized energy consumption at each base station and the averaged model accuracy of all users. Under our proposed solution, multiple edge base station are fully exploited to realize cost efficient personalized federated learning without any prior knowledge on model parameters. Simulation results show that our proposed strategy outperforms existing strategies in achieving accurate learning at low energy cost. [ABSTRACT FROM AUTHOR]

Induction motors are an important consumer of electricity. Among induction motors, speed-regulated induction motors have the greatest energy-saving potential. However, motors operating in a very wide speed range require a completely different approach than motors operating on a single supply frequency. An example of such motors are the drives of industrial washing machines. The article aimed to present how using field-circuit methods, but above all, analytical methods increase the efficiency of such a motor by changing its design. Thanks to the change of the design and the core material, it was possible to obtain the efficiency of the motor for the frequency of 10 and 20 Hz, meeting the requirements of the IE1 efficiency class specified for the frequency of 50 Hz, while for the frequency of 10 Hz it is 17.9% higher than in the currently produced motor, and for 20 Hz—by 11.1%, while for the frequency of 350 Hz the level of efficiency was corresponding to class IE4. The presented considerations may be the basis for such a procedure for other drives with similar properties. Additionally, an important element of the article is the refinement of the analytical method of determining core losses. [ABSTRACT FROM AUTHOR]

The maritime industry is experiencing a technological revolution that affects shipbuilding, operation of both seagoing and inland vessels, cargo management, and working practices in harbors. This ongoing transformation is driven by the ambition to make the ecosystem more sustainable and cost-efficient. Digitalization and automation help achieve these goals by transforming shipping and cruising into a much more cost- and energy-efficient and decarbonized industry segment. The key enablers in these processes are always-available connectivity and content delivery services, which can not only aid shipping companies in improving their operational efficiency and reducing carbon emissions, but also contribute to enhanced crew welfare and passenger experience. Due to recent advancements in integrating high-capacity and ultra-reliable terrestrial and non-terrestrial networking technologies, ubiquitous maritime connectivity is becoming a reality. To cope with the increased complexity of managing these integrated systems, this article advocates the use of artificial intelligence and machine-learning-based approaches to meet the service requirements and energy efficiency targets in various maritime communications scenarios. [ABSTRACT FROM AUTHOR]

Neural have achieved remarkable performance in computer vision, however, they are vulnerable to adversarial examples. Adversarial examples are inputs that have been carefully perturbed to fool classifier networks, while appearing unchanged to humans. Based on prior works on detecting adversaries, we propose a structured methodology of augmenting a deep neural network (DNN) with a detector subnetwork. We use adversarial noise sensitivity (ANS), a novel metric for measuring the adversarial gradient contribution of different intermediate layers of a network. Based on the ANS value, we append a detector to the most sensitive layer. In prior works, more complex detectors were added to a DNN, increasing the inference computational cost of the model. In contrast, our structured and strategic addition of a detector to a DNN reduces the complexity of the model while making the overall network adversarially resilient. Through comprehensive white-box and black-box experiments on MNIST, CIFAR-10, and CIFAR-100, we show that our method improves state-of-the-art detector robustness against adversarial examples. Furthermore, we validate the energy efficiency of our proposed adversarial detection methodology through an extensive energy analysis on various hardware scalable CMOS accelerator platforms. We also demonstrate the effects of quantization on our detector-appended networks. [ABSTRACT FROM AUTHOR]

Resistive random-access memory (ReRAM)-based architectures can be used to accelerate convolutional neural network (CNN) training. However, existing architectures either do not support normalization at all or they support only a limited version of it. Moreover, it is common practice for CNNs to add normalization layers after every convolution layer. In this work, we show that while normalization layers are necessary to train deep CNNs, only a few such layers are sufficient for effective training. A large number of normalization layers do not improve prediction accuracy; it necessitates additional hardware and gives rise to performance bottlenecks. To address this problem, we propose DeepTrain, a heterogeneous architecture enabled by a Bayesian optimization (BO) methodology; together, they provide adequate hardware and software support for normalization operations. The proposed BO methodology determines the minimum number of normalization operations necessary for a given CNN. Experimental evaluation indicates that the BO-enabled DeepTrain architecture achieves up to $15\times $ speedup compared to a conventional GPU for training CNNs with no accuracy loss while utilizing only a few normalization layers. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. In this paper, we present a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments (UEs) towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential eavesdropper called the untrusted UAV-relay (UUR). To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we devise an optimization framework in terms of a new measure $-$ secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UUR’s inherent flight power limitation into account. For the sake of quality of service fairness amongst all the UEs, we aim to maximize the minimum SEE (MSEE) performance via the joint design of key system parameters, including UUR’s trajectory and velocity, communication scheduling, and network power allocation. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we decouple it into four subproblems and propose alternative algorithms to solve it efficiently via greedy/sequential block successive convex approximation and non-linear fractional programming techniques. Numerical results demonstrate significant MSEE performance improvement of our designs compared to other known benchmarks. [ABSTRACT FROM AUTHOR]