This paper presents a comprehensive performance analysis and practical implementation of a parallel algorithm for constructing bifurcation diagrams of the fractional Selkov oscillator with variable coefficients and memory (SFO). The primary contribut...
Although neural network quantization is an imperative technology for the computation and memory efficiency of embedded neural network accelerators, simple post-training quantization incurs unacceptable levels of accuracy degradation on some important...
This study presents a neural network which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic...
Binary neural networks (BNNs) that use 1-bit weights and activations have garnered interest as extreme quantization provides low power dissipation. By implementing BNNs as computation-in-memory (CIM), which computes multiplication and accumulations o...
Processing-in-Memory (PIM) architectures are transforming system hierarchies by embedding computation within memory and collapsing traditional CPU–memory separation. This co-location enables measured gains in throughput and energy efficiency fo...
The prefetcher is one of the key performance subsystems in RISC-V processors, and its design can significantly enhance memory access efficiency, reduce latency, and improve overall processor performance. This paper conducts in-depth research on the d...
The Modified Discrete Sine Transform (MDST) is widely used in signal-processing applications where computational efficiency, latency, and energy consumption are critical design constraints. This paper introduces a low-complexity, parallel MDST algori...
Detecting available road space is a fundamental task for autonomous driving vehicles, requiring robust image feature extraction methods that operate reliably across diverse sensor-captured scenarios. However, existing approaches process each input in...
The compute-in-memory (CIM) which embeds computation inside memory is an attractive scheme to circumvent von Neumann bottlenecks. This study proposes a logic-compatible embedded DRAM architecture that supports data storage as well as versatile digita...
Computing-in-memory (CIM) with emerging non-volatile resistive memory devices has demonstrated remarkable performance in data-intensive applications, such as neural networks and machine learning. A crosspoint memory array enables naturally parallel c...
Eigenvalue problems play a fundamental role in many scientific and engineering disciplines, including structural mechanics, quantum physics, and control theory. In this paper, we propose a fast and stable fractional-order parallel algorithm for solvi...
A lot of research on deep learning and big data has led to efficient methods for processing large volumes of data and research on conserving computing resources. Particularly in domains like the IoT (Internet of Things), where the computing power is...
Binarized neural networks (BNNs), which have 1-bit weights and activations, are well suited for FPGA accelerators as their dominant computations are bitwise arithmetic, and the reduction in memory requirements means that all the network parameters ca...
Memristive logic-in-memory circuits can provide energy- and cost-efficient computing, which is essential for artificial intelligence-based applications in the coming Internet-of-things era. Although memristive logic-in-memory circuits have been previ...
Power efficiency is becoming a critical aspect of IoT devices. In this paper, we present a compact object-detection coprocessor with multiple cores for multi-scale/type classification. This coprocessor is capable to process scalable block size for mu...
With the exponential growth of big data, efficient groupby aggregation (GA) has become critical for real-time analytics across industries. GA is a key method for extracting valuable information. Current CPU-based solutions (such as large-scale parall...
The kNN machine learning method is widely used as a classifier in Human Activity Recognition (HAR) systems. Although the kNN algorithm works similarly both online and in offline mode, the use of all training instances is much more critical online tha...
Artificial intelligence (AI) has become ubiquitous in modern computing systems, from high-performance data centers to resource-constrained edge devices. As AI applications continue to expand into mobile and IoT domains, the need for energy-efficient...
The computation efficiency and flexibility of the accelerator hinder deep neural network (DNN) implementation in embedded applications. Although there are many publications on deep neural network (DNN) processors, there is still much room for deep op...
The emergence of exascale computing systems presents both opportunities and challenges in scientific computing, particularly for complex mathematical models requiring high-performance implementations. This paper addresses these challenges in the cont...
Since the Keccak algorithm was selected by the US National Institute of Standards and Technology (NIST) as the standard SHA-3 hash algorithm for replacing the currently used SHA-2 algorithm in 2015, various optimization methods have been studied in p...
Random number generation is essential in many application domains, including high-energy physics simulations. Implementing Monte Carlo methods that generate samples following a desired probability distribution is particularly challenging on hardware...
The explosive growth of data and information has increasingly motivated scientific and technological endeavors toward ultra-high-density data storage (UHDDS) applications. Herein, a donor−acceptor (D–A) type small conjugated molecule containing benzo...
To ensure the efficient operation of large-scale networks, the flow scheduling in the software defined network (SDN) requires the matching time and memory overhead of rule matching to be as low as possible. To meet the requirement, we solve the rule...
With the development of Convolutional Neural Networks (CNNs), there is a growing requirement for their deployment on edge devices. At the same time, Compute-In-Memory (CIM) technology has gained significant attention in edge CNN applications due to i...
This paper presents a computationally efficient state-of-health (SOH) estimator that is readily applicable to automotive battery management systems (BMSs). The proposed scheme uses a recursive estimator to improve the original scheme based on a batch...
The integration of High-Bandwidth Memory (HBM) into Field-Programmable Gate Arrays (FPGAs) has significantly enhanced data processing capabilities. However, the segmentation of HBM into 32 pseudo-channels, each managed by a performance-limited crossb...
This paper comparatively reviews sensing circuit designs for the most widely used embedded memory, static random-access memory (SRAM). Many sensing circuits for SRAM have been proposed to improve power efficiency and speed, because sensing operations...
The article discusses an approach to the construction and operation of a proactive system for protecting smart power grids against cyberattacks on service data transfer protocols. It is based on a combination of computational intelligence methods: id...
For data-intensive applications like edge AI and image processing, we present a new reconfigurable 8T SRAM-based in-memory computing (IMC) macro designed for high-performance and energy-efficient operation. This architecture mitigates von Neumann lim...
Field programmable gate array (FPGA) is widely considered as a promising platform for convolutional neural network (CNN) acceleration. However, the large numbers of parameters of CNNs cause heavy computing and memory burdens for FPGA-based CNN implem...
Neural networks are computing systems inspired by the biological neural networks in human brains. They are trained in a batch learning mode; hence, the whole training data should be ready before the training task. However, this is not applicable for...
This work discusses smart building applications involving the Internet of Things (IoT) which are focused on energy consumption monitoring and forecasting systems, as well as indoor air quality (IAQ) control. Low-cost hardware integrating sensors and...
The numerical solution for fractional dynamics problems can create a high computational load, which makes it necessary to implement efficient algorithms for their solution. The main contribution to the computational load of such computations is creat...
The direct porting of the Range Migration Algorithm to GPUs for three-dimensional (3D) cylindrical synthetic aperture radar (CSAR) imaging faces difficulties in achieving real-time performance while the architecture and programming models of GPUs sig...
At present, electron optical simulator (EOS) takes a long time to solve linear FEM systems. The algebraic multigrid preconditioned conjugate gradient (AMGPCG) method can improve the efficiency of solving systems. This paper is focused on the implemen...
High Efficiency Video Coding (HEVC) is the latest video standard developed by the Joint Video Exploration Team. HEVC is able to offer better compression results than preceding standards but it suffers from a high computational complexity. In particul...
With the development of information and communication technology, various types of Internet of Things (IoT) devices have widely been used for convenient services. Many users with their IoT devices request various services to servers. Thus, the amount...
In engineering applications, where we demand more and more precision, the modeling of systems with hysteretic nonlinearity has received considerable attention. The classical Preisach model (CPM) is currently the most popular for characterizing system...
Double-precision general matrix multiplication (DGEMM) is an essential kernel for measuring the potential performance of an HPC platform. ARMv8-based system-on-chips (SoCs) have become the candidates for the next-generation HPC systems with their hig...
(1) Background: Heartbeat detection from electrocardiogram (ECG) and photoplethysmograph (PPG) signals is widely used in wearable devices for health monitoring, fitness tracking, and stress assessment. While numerous methods have been proposed, their...
Algorithms for processing large-scale spatial datasets are of significant interest in both scientific research and industrial applications. The efficient implementation of such algorithms is crucial for modern data-intensive systems, and GPU-based pa...
The rapid expansion of medical data poses numerous challenges for Machine Learning (ML) tasks due to their potential to include excessive noisy, irrelevant, and redundant features. As a result, it is critical to pick the most pertinent features for t...
This work explores the application of Spiking Neural Networks (SNNs) and Continual Learning (CL) methodologies to the problem of steering angle regression, using autonomous driving simulation as the experimental context, with a focus on energy effici...
With the rapid development of artificial intelligence (AI) theory, particularly deep learning neural networks, robot vacuums equipped with AI power can automatically clean indoor floors by using intelligent programming and vacuuming services. To date...
In recent decades, memory-intensive applications have experienced a boom, e.g., machine learning, natural language processing (NLP), and big data analytics. Such applications often experience out-of-memory (OOM) errors, which cause unexpected process...
Many modern programmable embedded devices contain CPUs and a GPU that share the same system memory on a single die. Such a unified memory architecture (UMA) allows programmers to implement different communication models between CPU and the integrated...
Brain-inspired neuromorphic computing is expected to overcome the von Neumann bottleneck by eliminating the memory wall between processing and memory units. Nevertheless, critical challenges persist in synaptic device implementation, particularly reg...
Neuromorphic computing, a brain-inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to enhance performance while maintaining power efficiency. Neuromorphic c...
Considering the isotropic release process of nanoelectromechanical systems (NEMSs), defining the active region of NEM memory switches is one of the most challenging process technologies for the implementation of monolithic-three-dimensional (M3D) CMO...
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