Memristive crossbar array-based hardware framework for compressed sensing and event-driven neuromorphic processing.

The recently proposed compressive sensing (CS) theory provides a new solution for multiple description coding (MDC) with fine granularity, by treating each random CS measurement as a description. The performance of CS-based MDC (CS-MDC) depends on the efficacy of the CS recovery algorithm. Existing CS recovery algorithms recover the signal in a fixed space (e.g., Wavelet, DCT, and gradient spaces) for the entire duration of the signal, even though a typical multimedia signal exhibits sparsity in time/space variant spaces. To rectify this problem and develop a better CS recovery algorithm for CSMDC, we propose a learning-based framework to conduct the CS recovery in locally adaptive spaces, and carry out a case study on image MDC. A set of prior image models are learned offline from a training set to facilitate the CS recovery in local adaptive bases. Experiments show that the learning-based CS recovery algorithm can significantly improve the performance of the previous CS-MDC technique in both PSNR and visual quality.

With the proliferation of IoT and edge devices, massive amounts of heterogeneous data are generated in real time. Efficient compression?decompression mechanisms are essential to reduce storage overhead, minimize transmission latency, and optimize energy consumption. However, traditional compression algorithms lack adaptability to dynamic environments where resource constraints and workload patterns vary rapidly. This research proposes a Deep Reinforcement Learning (DRL)-based adaptive framework for real-time compression?decompression optimization in edge devices. The agent dynamically selects compression levels, algorithms, and bit allocation strategies based on current device constraints (CPU, memory, bandwidth, and energy) and application-specific quality requirements. Experimental evaluations show that the DRL-based approach achieves up to 40% reduction in latency and 30% improvement in energy efficiency, while maintaining near-lossless reconstruction accuracy compared to state-of-the-art baselines.

Fully homomorphic encryption (FHE) enables direct computation on encrypted data without decryption, ensuring data privacy in cloud computing scenarios and preventing the leakage of sensitive information. However, the computational overhead of HE typically exceeds that of plaintext computation by 4 to 5 orders of magnitude, while energy consumption is 5 to 6 orders of magnitude higher. These substantial performance and energy overheads significantly hinder the widespread adoption of FHE. This paper proposed LP-HENN, a novel low-power and energy-efficient FHE accelerator architecture that leverages a RISC-V vector coprocessor and ReRAM crossbar arrays. LP-HENN targets power-constrained application scenarios such as edge devices, aiming to provide highly energy-efficient acceleration support for FHE applications. LP-HENN leverages the collaborative work of the vector processor and ReRAM crossbars, employing optimization strategies to achieve full pipelining and minimize memory access. Furthermore, this paper proposed a parameter selection model for early-stage architecture design, which achieves an optimal balance between performance and energy consumption through the collaborative optimization of multiple parameters. Experimental results show that, for an FHE-based convolutional neural network (HE-CNN) inference application, LP-HENN achieves a 31.82Ã- and 11920.56Ã- improvement in performance and energy efficiency, respectively, compared to CPU. Compared to FxHENN, the state-of-the-art FPGA-based FHE accelerator with high energy efficiency for edge devices, LP-HENN achieves a 2.36Ã- and 10.04Ã- improvement in performance and energy efficiency, respectively. The energy efficiency of LP-HENN is comparable to that of F1, the state-of-the-art ASIC FHE accelerator, while featuring a low power design suitable for edge computing.

Energy Efficiency plays a vital role in Wireless Sensor Networks (WSN) because of the limited battery power of nodes. Data Aggregation techniques help in reducing the redundant data transmissions which results in reduced energy consumption. Compressive Sensing (CS) is one of the effective signal processing techniques which helps in reducing the number of transmissions resulting in improved energy efficiency. Clustering is also a proven method of energy efficient data transmission. Employing the CS technique in cluster based WSN gains advantages of both and the outcome of which is further reduction in energy consumption. In this paper, a review of algorithms which employ CS in cluster based WSN is made and explanation of the same is given in brief. Some algorithms employ plain-CS and some employ hybrid-CS techniques. Using the sparse representation of the data vector in some orthonormal basis, even with smaller number of measurements, the data can be reconstructed accurately. First, we have given the details of compressive sensing and the possible sparse transforms, measurement matrices and data reconstruction algorithms. Then, recent works which exploit CS in clustering algorithms are discussed and comparison of the same is provided.

In compressive sensing (CS), a challenge is to find a space in which the signal is sparse and, hence, faithfully recoverable. Since many natural signals such as images have locally varying statistics, the sparse space varies in time/spatial domain. As such, CS recovery should be conducted in locally adaptive signal-dependent spaces to counter the fact that the CS measurements are global and irrespective of signal structures. On the contrary, existing CS reconstruction methods use a fixed set of bases (e.g., wavelets, DCT, and gradient spaces) for the entirety of a signal. To rectify this problem, we propose a new framework for model-guided adaptive recovery of compressive sensing (MARX) and show how a 2-D piecewise autoregressive model can be integrated into the MARX framework to make CS recovery adaptive to spatially varying second order statistics of an image. In addition, MARX offers a mechanism of characterizing and exploiting structured sparsities of natural images, greatly restricting the CS solution space. Simulation results over a wide range of natural images show that the proposed MARX technique can improve the reconstruction quality of existing CS methods by 2-7 dB.

In the rapidly advancing field of the Internet of Things (IoT), the capability to process data in real-time within edge devices that have limited computational and energy resources remains a significant challenge. Traditional methods of data acquisition and processing often fail to meet these demands, leading to inefficiencies and compromised data integrity. Addressing this critical gap, our paper introduces three innovative compressed sensing algorithms specifically designed for IoT applications: Structured Random Compressed Sampling Matching Pursuit (SRCoSaMP), Sparse Adaptive Reconstruction Scheme (SPARS), and Real Time Sparse IoT (RTSI). These algorithms are specially designed to process data quickly and effectively, despite the limited resources available on edge devices. We delve into the intricate design and mathematical foundations of each algorithm, emphasizing their adaptability, real-time processing capabilities, and energy efficiency. Empirical evaluations demonstrate their superior performance in terms of real-time data processing efficiency, recovery accuracy, and computational resource management. The findings of our research mark a significant step forward in the domain of IoT data processing, offering robust solutions that ensure data integrity with minimal data samples.

High Energy efficiency and high performance are the key regiments for Internet of Things (IoT) edge devices. Exploiting cluster of multiple programmable processors has recently emerged as a suitable solution to address this challenge. However, one of the main power bottlenecks for multi-core architectures is the instruction cache memory. We propose a two-level structure based on Standard Cell Memories (SCMs) which combines a private instruction cache (L1) per-core and a low-latency (only one cycle latency) shared instruction cache (L1,5). We present a detailed comparison of performance and energy efficiency for different instruction cache architectures. Our system-level analysis shows that the proposed design improves upon both state-of-the art private and shared cache architectures and balances well performance with energy-efficacy. On average, when executing a set of real-life IoT applications, our multi-level cache improves performance and energy efficiency both by 10% with respect to the private instruction cache system, and improves energy efficiency by 15% and 7% with a performance loss of only 2% with respect to the shared instruction cache. Besides, relaxed timing makes two-level instruction cache an attractive choice for aggressive implementation, with more slack for convergence in physical design.

Recently, Compressive Sensing (CS) has been applied to array signal processing. In theory, Direction-of-Arrival (DOA) estimation based on CS recovery can work well in correlated environments. However, a large number of sensors (i.e., linear measurements) are still needed for CS recovery. To improve on this, we propose a new CS-based DOA estimation method with a recently designed antenna structure called the Asymmetric Antenna without Phase Center (Asym-AWPC). The best reconstruction is achieved by solving the l1-norm optimization problem, which is cast as an l1-regularized least-squares program. Simulated results indicate the effectiveness of the proposed CS-based Asym-AWPC DOA estimator in a multipath environment over a recent Asym-AWPC DOA estimator but using the Multiple Signal Classification (MUSIC) rather than CS. Further improvement on the resolution can be achieved by tuning the degree of asymmetry in designing the Asym-AWPC.

10 - Improving energy efficiency in next-generation IoT networks with beam adjustment technique

The increasing demand for higher data rates pushes the boundaries of the currently used radio spectrum. The terahertz (THz) frequency band (0.1-10 THz) is widely considered by scientific community to address spectrum scarcity. In this paper we developed a theoretical model for device-to-device (D2D) communication operating in THz band. We also derived a close form formula of data rates, outage probability, and energy efficiency. Our simulation results show an improvement of data rates and energy efficiency while decreasing the outage probability of D2D communication in THz. For instance, there is 86% of increase in energy efficiency when the transmission power is 19dBm. Finally, the improvement of energy efficiency is 87% using optimal transmission power due to 50 resource blocks.

Learning from nature for healthcare, energy, and environment

In this paper, the optimization of energy efficiency (EE) and the improvement of user data rate in cloud radio access networks (C-RANs) are studied by introducing a weighted sparse beamforming (WSB) method. Using stochastic geometry tools, analytical expressions are derived to describe downlink ergodic rate and coverage probability for coordination multipoint joint transmission in C-RAN. Based on these expressions and by taking advantage of central processing and coordination in baseband unit pool, power allocation among remote radio heads is optimized using the WSB. The WSB optimization algorithm is based on water-filling power allocation strategy and redistributing the power among RRHs. The improvement in both EE and ergodic rate is achieved. The improvement in EE is pronounced up to 28% using WSB technique compare to the equal power beamforming method in the proposed C-RAN network.

There is an increasing demand for running neural network inference on edge devices. Memristor crossbar array (MCA) based accelerators can be used to accelerate neural networks on edge devices. However, reliability issues in memristors, such as stuck-at faults (SAF) and variations, lead to weight deviation of neural networks and therefore have severe influence on inference accuracy. In this work, we focus on reliability issues for edge devices. We formulate the reliability problem as a 0-1 programming problem, based on the analysis of sum weight variation (SWV). In order to solve the problem, we simplify the problem with an approximation - different columns have the same weights - based on our observation of the weight distribution. Then we propose an effective mapping method to solve the simplified problem. The experimental results show that our proposed method can recover 95% accuracy considering SAF defects and can increase by up to 60% accuracy in variation σ=0.4.

Improvement of energy efficiency has been a major public policy concern in Korea. Investment cost for location of avoidance facility such as nuclear power and radioactive waste management facility, environmental pollution etc could significantly reduced from improvement of energy efficiency. Energy efficiency improvement could be one of good solution to cope with volatile energy market. Korea has been high oversea dependency in fossil fuels supply to meet its energy requirement. Its import dependency on fossil fuels in 1995 and 2000 is respectively 96.8% and 97.2%. The fact that Korea depends heavily on imported energy shows it should make more efforts to promote improvement of energy efficiency. Although Korean Government has supported industrial sector in improvement of energy efficiency, some energy analysts and critics point out that Korea has little progressed in energy efficiency improvement. This paper estimated its energy efficiency and analyzed its improvement with energy Input- Output table 90-95-2000 of Korea. The results are appeared differently. The cross section and time series analysis result of the energy efficiency is that energy elasticity for GDP, energy intensity and induced energy consumption between 1995 and 2000 significantly improved. From 1990 to 1995, energy elasticity for GDP is bigger than 1. It means that energy consumption increased rapidly more than GDP growth rate. This is the evidence that energy efficiency was not improved. Energy intensity of each industrial sector between 1995 and 2000 is lower than in cases of 1990 and 1995. It shows that energy efficiency was improved. As energy intensity of each industry becomes lower, induced energy consumption of economy goes smaller because of interaction among sectors.

Compressed Sensing (CS) exploits sparsity of images to reconstruct them exactly from a small set of measurements. Recent studies have shown that nonlocal sparsity leads to superior results in CS recovery. In this paper, we first propose a new sparsity model, which is characterized as a function of the coefficients achieved by transforming the 3D array generated from a set of similar image patches. The function which we use is the nonconvex Garrote function. To efficiently solve the proposed corresponding optimization problem, we then employ an accelerated algorithm based on iterative proximal methods. Our reconstruction method exploits the benefits of both nonlocal sparsity and nonconvex optimization. Experimental results show effectiveness of the proposed method compared with the many existing algorithms in CS image recovery.