Energy consumption of processors and memories is quickly becoming a limiting factor in the deployment of large computing systems. For this reason, it is important to understand the energy performance of these processors and to study strategies allowi...
This paper presents an on-chip implementation of an analog processor-in-memory (PIM)-based convolutional neural network (CNN) in a biosensor. The operator was designed with low power to implement CNN as an on-chip device on the biosensor, which consi...
Fully Homomorphic Encryption (FHE) allows computations on encrypted data without decryption, providing strong security for sensitive information. However, computational and memory demands for FHE are significant challenges, particularly in the Number...
Recently, frequent data movement between computing units and memory during floating-point arithmetic has become a major problem for scientific computing. Computing-in-memory (CIM) is a novel computing paradigm that merges computing logic into memory,...
The high parameter and memory access demands of CNNs highlight the need to reduce off-chip memory accesses. While recent approaches have improved data reuse to lessen these accesses, simple and efficient prefetching methods are still lacking. This pa...
To address the concerns with power consumption and processing efficiency in big-size data processing, sparse coding in computing-in-memory (CIM) architectures is gaining much more attention. Here, a novel Flash-based CIM architecture is proposed to i...
As artificial intelligence (AI) advances, deep learning models are shifting from convolutional architectures to transformer-based structures, highlighting the importance of accurate floating-point (FP) calculations. Compute-in-memory (CIM) enhances m...
Ternary content-addressable memories (TCAMs) are used to design high-speed search engines. TCAM is implemented on application-specific integrated circuit (native TCAMs) and field-programmable gate array (FPGA) (static random-access memory (SRAM)-base...
Video stabilization is one of the most important features in consumer cameras. Even simple video stabilization algorithms may need to access the frames several times to generate a stabilized output image, which places a significant burden on the came...
In this work, the implementation of HfZrO layers for the tunneling, charge trapping, and blocking mechanisms within the device offer benefits in terms of programmability and data retention. This configuration has resulted in a memory device that can...
The security of lattice-based cryptosystems is based on solving hard lattice problems such as the shortest vector problem (SVP) and the closest vector problem (CVP). Various cryptanalysis algorithms such as (Pro)GaussSieve, HashSieve, ENUM, and BKZ h...
The density and cost-effectiveness of flash memory chips continue to increase, driven by: (a) The continuous physical scaling of memory cells in a single layer; (b) The vertical stacking of multiple layers; and (c) Logical scaling through storing mul...
This article presents a finite impulse response (FIR) moving-average filter for validating noisy analog signals. While the conventional sliding-window implementation is computationally efficient, it requires storing the last N − 1 samples, lead...
Efficient management and utilization of edge server memory buffers are crucial for improving the efficiency of concurrent editing in the concurrent editing application scenario of large-scale video in edge computing. In order to elevate the efficienc...
Wireless Sensor Networks (WSNs) are often deployed in hostile environments and, thus, nodes can be potentially captured by an adversary. This is a typical white-box attack context, i.e., the adversary may have total visibility of the implementation o...
The emerging resistive switching devices have attracted broad interest as promising candidates for future memory and computing applications. Particularly, it is believed that memristor-based neuromorphic engineering promises to enable efficient artif...
Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications includi...
Processing-in-memory (PIM) is a promising architecture to design various types of neural network accelerators as it ensures the efficiency of computation together with Resistive Random Access Memory (ReRAM). ReRAM has now become a promising solution...
Currently, research on the lattice Boltzmann method mainly focuses on its numerical simulation and applications, and there is an increasing demand for large-scale simulations in practical scenarios. In response to this situation, this study successfu...
The traditional computer architectures severely suffer from the bottleneck between the processing elements and memory that is the biggest barrier in front of their scalability. Nevertheless, the amount of data that applications need to process is inc...
The speed of modern digital systems is severely limited by memory latency (the “Memory Wall” problem). Data exchange between Logic and Memory is also responsible for a large part of the system energy consumption. Logic-in-Memory (LiM) represents an a...
The implementation of neural networks (NNs) on edge devices enables local processing of wireless data, but faces challenges such as high computational complexity and memory requirements when deep neural networks (DNNs) are used. Shallow neural networ...
Remote direct memory access (RDMA)-based distributed memory systems improve cluster-wide memory utilization by allowing for memory-constrained nodes to access unused memory on remote nodes. However, most existing systems adopt a memory-constrained-no...
Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include...
Software quantum simulators are essential tools for designing and testing quantum algorithms on classical computing architectures, especially given the current limitations of physical quantum hardware. This work focuses on studying and evaluating mem...
This paper describes a hardware/software strategy for the effective and efficient management of several distributed Memory Built-In Self-Test (MBIST) units orchestrated by a single CPU to enable the parallel testing of several memory banks. Experimen...
Multi-level cell (MLC) phase change memory (PCM) can not only effectively multiply the memory capacity while maintaining the cell area, but also has infinite potential in the application of the artificial neural network. The write and verify scheme i...
Recent advances in nanoscale resistive memory devices offer promising opportunities for in-memory computing with their capability of simultaneous information storage and processing. The relationship between current and memory conductance can be utili...
In modern information technology systems, secure storage and transmission of personal and sensitive data are recognized as important tasks. These requirements are achieved through secure and robust encryption methods. Argon2 is an advanced cryptograp...
With the development of remote sensing technology and very large-scale integrated circuit (VLSI) technology, the real-time processing of spaceborne Synthetic Aperture Radar (SAR) has greatly improved the ability of Earth observation. However, the cha...
In the history of access control, nearly every system designed has relied on the operating system (OS) to enforce the access control protocols. However, if the OS (and specifically root access) is compromised, there are few if any solutions that can...
Resistive switching devices (memristors) constitute a promising device technology that has emerged for the development of future energy-efficient general-purpose computational memories. Research has been done both at device and circuit level for the...
As the scope of artificial intelligence (AI) expands and the structure becomes more complex, the amount of data for inference and training has increased. In traditional computer architectures, the memory bandwidth limitations have intensified bottlen...
The Non-Volatile Memory (NVM), such as PRAM or STT-MRAM, is often adopted as the main memory in portable embedded systems. The non-volatility triggers a security issue against physical attacks, which is a vulnerability caused by memory extraction and...
Deep neural networks (DNNs) require efficient hardware accelerators due to the high cost of vector–matrix multiplication operations. Computing-in-memory (CIM) architectures address this challenge by performing computations directly within memor...
This article investigates the efficiency of hybridizing composites with thin layers of martensitic shape memory alloys for improvement of damping. The non-linear damping behaviour of martensitic shape memory alloys is simulated using a modified versi...
Low-density parity-check (LDPC) codes are known to be one of the best error-correction coding (ECC) schemes in terms of correction performance. They have been utilized in many advanced data communication standards for which the codecs are typically i...
Structured grid-based sparse matrix-vector multiplication and Gauss–Seidel iterations are very important kernel functions in scientific and engineering computations, both of which are memory intensive and bandwidth-limited. GPDSP is a general p...
Recently, due to the advent of resource-constrained trends, such as smartphones and smart devices, the computing environment is changing. Because our daily life is deeply intertwined with ubiquitous networks, the importance of security is growing. A...
In nuclear engineering, the
Recent advancements in memory technology have opened up a wealth of possibilities for innovation in data structures. The emergence of byte-addressable persistent memory (PM) with its impressive capacity and low latency has accelerated the adoption of...
On-chip memory is one of the core components of deep learning accelerators. In general, the area used by the on-chip memory accounts for around 30% of the total chip area. With the increasing complexity of deep learning algorithms, it will become a c...
The storage and processing of remotely sensed hyperspectral images (HSIs) is facing important challenges due to the computational requirements involved in the analysis of these images, characterized by continuous and narrow spectral channels. Althoug...
The model called Finite State Machine with Input Multiplexing (FSMIM) was proposed as a mechanism for implementing Finite State Machines (FSMs) using ROM memory. This paper presents a novel approach for achieving more efficient FSMIM implementations...
In this paper, we describe an actuator-based EMDR (eye movement desensitization and reprocessing) virtual assistant system that can be used for the treatment of participants with traumatic memories. EMDR is a psychological therapy designed to treat e...
Differential Computation Analysis (DCA) leverages memory traces to extract secret keys, bypassing countermeasures employed in white-box designs, such as encodings. Although researchers have made great efforts to enhance security against DCA, most sol...
Copy-on-Write (CoW) is one of the most essential memory management techniques enabling efficient page sharing between processes. Specifically, combined CoW with the fork system call, applications, even with a huge memory footprint, can take a snapsho...
Memory isolation is an essential technology for safeguarding the resources of lightweight embedded systems. This technique isolates system resources by constraining the scope of the processor’s accessible memory into distinct units known as dom...
Real-time communication has witnessed a dramatic increase in recent years in user daily usage. In this domain, Session Initiation Protocol (SIP) is a well-known protocol found to provide trusted services (voice or video) to end users along with effic...
Deep learning (DL) models have achieved remarkable progress in natural language processing, computer vision, content generation, and edge intelligence; however, their rapidly increasing computational complexity, memory demand, and deployment diversit...
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