IEEE Embedded Systems Letters最新文献_第4页

DynHD: Hyperdimensional Computing Approach for Efficient Radar Spectrum Classification 高效雷达频谱分类的超维计算方法

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-10-24 DOI: 10.1109/LES.2024.3485638

Zhuowen Zou;Yang Ni;SungHeon Jeong;Satish Ravindran;Binbin Shi;Phil Chen;Mohsen Imani

{"title":"DynHD: Hyperdimensional Computing Approach for Efficient Radar Spectrum Classification","authors":"Zhuowen Zou;Yang Ni;SungHeon Jeong;Satish Ravindran;Binbin Shi;Phil Chen;Mohsen Imani","doi":"10.1109/LES.2024.3485638","DOIUrl":"https://doi.org/10.1109/LES.2024.3485638","url":null,"abstract":"Radar technology plays a critical role in target detection, classification, and tracking. However, the computational demands of training deep neural networks (DNNs) on radar signals can be overwhelming, posing challenges for edge devices with limited energy and computing resources. In this article, we propose leveraging hyperdimensional computing (HDC), a brain-inspired computing paradigm, as an efficient alternative. HDC utilizes high-dimensional vectors for information representation and processing, offering robustness and energy efficiency. We propose a novel HDC classification algorithm named DynHD, with a dynamic HDC encoder that adapts to more challenging radar spectrum recognition tasks. We designed this mechanism to provide great flexibility to the HDC encoder that is otherwise fixed. Our evaluations demonstrate that HDC-based approaches achieve comparable accuracy to DNN-based methods with lower-computational complexity, making them suitable for resource-constrained devices. We achieve significant improvements in latency during training and inference phases, enabling efficient processing of radar signals on edge devices.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"17 2","pages":"95-98"},"PeriodicalIF":1.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Optimized Structure for Data Alignment Logic in Parallel CRC Computing Circuits 并行CRC计算电路中数据对齐逻辑的优化结构

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-10-24 DOI: 10.1109/LES.2024.3485632

Yuchen Nie;Sheng Zhong;Nailiang Kuang;Yuting Ji;Hangzai Luo

引用次数: 0

PRIOT: Pruning-Based Integer-Only Transfer Learning for Embedded Systems 基于剪枝的嵌入式系统全整数迁移学习

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-10-23 DOI: 10.1109/LES.2024.3485003

Honoka Anada;Sefutsu Ryu;Masayuki Usui;Tatsuya Kaneko;Shinya Takamaeda-Yamazaki

{"title":"PRIOT: Pruning-Based Integer-Only Transfer Learning for Embedded Systems","authors":"Honoka Anada;Sefutsu Ryu;Masayuki Usui;Tatsuya Kaneko;Shinya Takamaeda-Yamazaki","doi":"10.1109/LES.2024.3485003","DOIUrl":"https://doi.org/10.1109/LES.2024.3485003","url":null,"abstract":"On-device transfer learning is crucial for adapting a common backbone model to the unique environment of each edge device. Tiny microcontrollers, such as the Raspberry Pi Pico, are key targets for on-device learning but often lack floating-point units, necessitating integer-only training. Dynamic computation of quantization scale factors, which is adopted in former studies, incurs high computational costs. Therefore, this letter focuses on integer-only training with static-scale factors, which is challenging with existing training methods. We propose a new training method named PRIOT, which optimizes the network by pruning selected edges rather than updating weights, allowing effective training with static-scale factors. The pruning pattern is determined by the edge-popup algorithm, which trains a parameter named score assigned to each edge instead of the original parameters and prunes the edges with low scores before inference. Additionally, we introduce a memory-efficient variant, PRIOT-S, which only assigns scores to a small fraction of edges. We implement PRIOT and PRIOT-S on the Raspberry Pi Pico and evaluate their accuracy and computational costs using a tiny CNN model on the rotated MNIST dataset and the VGG11 model on the rotated CIFAR-10 dataset. Our results demonstrate that PRIOT improves accuracy by 8.08–33.75 percentage points over existing methods, while PRIOT-S reduces memory footprint with minimal accuracy loss.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"17 2","pages":"87-90"},"PeriodicalIF":1.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel One-Versus-All Approach for Multiclass Classification in TinyML Systems TinyML系统中多类分类的一种单对全新方法

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-10-16 DOI: 10.1109/LES.2024.3482002

Tobiasz Puślecki;Krzysztof Walkowiak

引用次数: 0

Analysis of S-Box Hardware Resources to Improve AES Intrinsic Security Against Power Attacks S-Box硬件资源分析提高AES抗电源攻击的内在安全性

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-10-10 DOI: 10.1109/LES.2024.3478070

Thockchom Birjit Singha;Roy Paily Palathinkal;Shaik Rafi Ahamed

{"title":"Analysis of S-Box Hardware Resources to Improve AES Intrinsic Security Against Power Attacks","authors":"Thockchom Birjit Singha;Roy Paily Palathinkal;Shaik Rafi Ahamed","doi":"10.1109/LES.2024.3478070","DOIUrl":"https://doi.org/10.1109/LES.2024.3478070","url":null,"abstract":"Side-channel attacks (SCAs) have rendered Internet of Things (IoT)-based devices unsafe despite employing Advanced Encryption Standard (AES) as the cryptographic algorithm. Additional circuitry, called countermeasures, is used to protect AES against the attacks at the cost of huge area and power overheads. The attacks are performed on SubBytes round operation of AES, which comprises of 16 S-boxes. This letter makes a novel attempt to boost the intrinsic security of an unprotected AES by analyzing four smallest composite field arithmetic (CFA)-based S-boxes available in literature, Circuit Minimization Team (CMT), Canright, Maximov, and Masoleh with lookup table (LUT)-based S-box as a reference. This letter proposes an AES design which is unprotected but with enhanced security. The designer can aim higher security by adding smaller countermeasure protective schemes before incorporating into IoT devices. A novel 3-D hardware analysis, namely, hardware resources, hardware complexity/linearity, and hardware security, is performed which demonstrates that lesser gate equivalent (GE) and linear gates of Masoleh S-box offer the highest security. Upon evaluation on Side-Channel Attack Standard Evaluation Board (SASEBO), all the hardware security metrics favored Masoleh S-box, depicting nearly \u0000<inline-formula> <tex-math>$94 times $ </tex-math></inline-formula>\u0000 gain in security and 80% reduction in area with respect to other unprotected designs.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"16 4","pages":"525-528"},"PeriodicalIF":1.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simplified Oblique Decision Tree Accelerator 简化倾斜决策树加速器

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-10-07 DOI: 10.1109/LES.2024.3475397

Rituparna Choudhury;Shaik Rafi Ahamed;Prithwijit Guha

引用次数: 0

Implementation of Polyphase Digital Down Converter Using Optimized LMS Algorithm for WCDMA Application 基于优化LMS算法的WCDMA多相数字下变频实现

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-10-07 DOI: 10.1109/LES.2024.3473539

Debarshi Datta;Mrinal Kanti Naskar

引用次数: 0

Using Intermittent Chaotic Clocks to Secure Cryptographic Chips 使用间歇混沌时钟保护密码芯片

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-10-07 DOI: 10.1109/LES.2024.3472709

Abdollah Masoud Darya;Sohaib Majzoub;Ali A. El-Moursy;Mohamed Wed Eladham;Khalid Javeed;Ahmed S. Elwakil

引用次数: 0

TinyML-Based Intrusion Detection System for In-Vehicle Network Using Convolutional Neural Network on Embedded Devices 基于tinyml的嵌入式设备卷积神经网络车载网络入侵检测系统

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-10-07 DOI: 10.1109/LES.2024.3475470

Hyungchul Im;Seongsoo Lee

{"title":"TinyML-Based Intrusion Detection System for In-Vehicle Network Using Convolutional Neural Network on Embedded Devices","authors":"Hyungchul Im;Seongsoo Lee","doi":"10.1109/LES.2024.3475470","DOIUrl":"https://doi.org/10.1109/LES.2024.3475470","url":null,"abstract":"This letter proposes a novel model for effectively detecting malicious messages in controller area network (CAN) communication, which is widely used in automotive networks. Because in-vehicle networks operate in resource-constrained environments, an intrusion detection system (IDS) must simultaneously provide a low computational load and excellent detection performance. However, existing models are unsuitable for deployment on low-power embedded devices owing to their high computational requirements. This letter presents a low-complexity convolutional neural network (CNN)-based IDS for deployment on embedded edge devices. The proposed model applies CNN operations separately to the CAN ID sequence and the data field of the CAN frame to extract features and concatenate them for feature fusion. Experimental results demonstrate that this approach requires considerably less computational load and provides superior detection performance. Furthermore, the proposed model is deployed on a resource-constrained nRF52840 microcontroller using TensorFlow Lite for Microcontrollers with 20.44-kB flash memory and 26.44-kB RAM without quantization.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"17 2","pages":"67-70"},"PeriodicalIF":1.7,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MQTT-Based Adaptive Estimation Over Distributed Network Using Raspberry Pi Pico W 基于mqtt的分布式网络自适应估计

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-10-03 DOI: 10.1109/LES.2024.3473017

Prantaneel Debnath;Anshul Gusain;Parth Sharma;Pyari Mohan Pradhan

{"title":"MQTT-Based Adaptive Estimation Over Distributed Network Using Raspberry Pi Pico W","authors":"Prantaneel Debnath;Anshul Gusain;Parth Sharma;Pyari Mohan Pradhan","doi":"10.1109/LES.2024.3473017","DOIUrl":"https://doi.org/10.1109/LES.2024.3473017","url":null,"abstract":"As the demand for edge computing applications continues to rise, the need for efficient training of resource-constrained devices becomes paramount. This letter proposes message queuing telemetry transport (MQTT)-based implementation of distributed estimation strategies in the context of the Internet of Things (IoT), namely incremental, consensus, and diffusion strategies. The use of Raspberry Pi Pico W in the emulation environment is motivated by its advanced capability, while the MQTT data protocol is employed to address the constraints associated with conventional HTTP/HTTPs protocols. Synchronization in an IoT network is achieved by the integration of a novel methodology that entails the use of the wait-for-slowest (WFS) protocol and the MQTT protocol. Furthermore, the development of a graphical user interface supported by the Django application allows for adjusting parameters in distributed strategies through the HTTP REST API, along with SQLite. The results acquired from hardware experiments exhibit a strong correlation between the mean-square performance achieved from simulation studies. The distributed estimation strategy is compared with state-of-the art centralized and noncooperation estimation strategies, demonstrating its superior performance. In addition, a study is conducted on the resilience of these IoT networks in the face of several network threats, such as node failure and model poisoning attacks. A theoretical analysis is provided to explain the relationship between the number of iterations and node failure.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"16 4","pages":"517-520"},"PeriodicalIF":1.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0