IEEE Embedded Systems Letters最新文献_第9页

LOTUS: A Scalable Framework to Lock Multimodule Designs With One-Time Self-Destructing Key LOTUS：利用一次性密钥和自毁方法锁定多模块设计的可扩展框架

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-02-05 DOI: 10.1109/LES.2024.3360615

Mona Hashemi;Siamak Mohammadi;Trevor E. Carlson

引用次数: 0

ProMiSE: A Programmable Hardware Monitor for Secure Execution in Zero Trust Networks PROMISE：零信任网络中安全执行的可编程硬件监控器

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-01-16 DOI: 10.1109/LES.2024.3354831

Nikhilesh Singh;Shagnik Pal;Rainer Leupers;Farhad Merchant;Chester Rebeiro

引用次数: 0

Middleton Class A Noise Median Estimator: FPGA and Software Implementation Middleton A 级噪声中值估计器：FPGA 和软件实现

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-01-15 DOI: 10.1109/LES.2024.3354179

Lucas A. Rabioglio;M. C. Cebedio;L. Arnone;L. De Micco;J. Castiñeira Moreira

引用次数: 0

SoC-Based Implementation of 1-D Convolutional Neural Network for 3-Channel ECG Arrhythmia Classification via HLS4ML 通过 HLS4ML 实现基于 SoC 的用于 3 通道心电图心律失常分类的一维卷积神经网络

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-01-15 DOI: 10.1109/LES.2024.3354081

Feroz Ahmad;Saima Zafar

{"title":"SoC-Based Implementation of 1-D Convolutional Neural Network for 3-Channel ECG Arrhythmia Classification via HLS4ML","authors":"Feroz Ahmad;Saima Zafar","doi":"10.1109/LES.2024.3354081","DOIUrl":"10.1109/LES.2024.3354081","url":null,"abstract":"Real-time monitoring of 1-D biopotentials, such as electrocardiograms (ECG), necessitates effective feature extraction and classification, a strength of deep learning (DL) algorithms. Designing 1-D convolutional neural network (1-D CNN) accelerators for biopotential classification via open-source codesign workflows, particularly high-level synthesis for machine learning (HLS4ML), offers advantages over GPU-based or cloud-based solutions, including high performance, low latency, low power consumption, swift time-to-market, and cost-effectiveness. We present an implementation of a quantized-pruned (QP) 1-D CNN model on the PYNQ Z2 SoC using HLS4ML by seamlessly deploying its soft IP core generated via Vivado Accelerator backend, showcasing the efficacy of quantization-aware training (QAT) in reducing power consumption to 1.655 W from 1.823 W. Our approach demonstrates improved area consumption, resource utilization, and inferences per second compared to the baseline (B) 1-D CNN model, with a controlled 4% or less reduction in weighted Accuracy, Precision, Recall, and F1-score, revealing the nuanced tradeoffs between performance metrics and system efficiency for real-time 3-channel ECG Arrhythmia classification.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"16 4","pages":"429-432"},"PeriodicalIF":1.7,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139954458","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

An Area and Energy Efficient Serial-Multiplier 面积和能效比高的串行乘法器

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-01-10 DOI: 10.1109/LES.2024.3352540

Mohd. Tasleem Khan;Jinti Hazarika

引用次数: 0

Adaptive Kernel Merge and Fusion for Multi-Tenant Inference in Embedded GPUs 嵌入式 GPU 多租户推理的自适应内核合并与融合

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-01-09 DOI: 10.1109/LES.2024.3351753

Jaebeom Jeon;Gunjae Koo;Myung Kuk Yoon;Yunho Oh

{"title":"Adaptive Kernel Merge and Fusion for Multi-Tenant Inference in Embedded GPUs","authors":"Jaebeom Jeon;Gunjae Koo;Myung Kuk Yoon;Yunho Oh","doi":"10.1109/LES.2024.3351753","DOIUrl":"10.1109/LES.2024.3351753","url":null,"abstract":"This letter proposes a new scheme that improves throughput and reduces queuing delay while running multiple inferences in embedded graphics processing unit (GPU)-based systems. We observe that an embedded system runs inference with a fixed number of deep learning models and that inference requests often use the same model. Unlike prior work that proposed kernel fusion or scheduling techniques, this letter proposes a new software technique that merges and fuses kernels by monitoring the requests in a queue. The proposed technique first monitors a fixed number of requests and groups the requests running the same model. Then, it creates the kernels that iteratively process the grouped requests. We call such a technique kernel merging. After that, the proposed technique performs kernel fusion with merged kernels. Eventually, our idea minimizes the number of concurrent kernels, thus mitigating stalls caused by frequent context switching in a GPU. In our evaluation, the proposed kernel merge and fusion achieve \u0000<inline-formula> <tex-math>$2.7times $ </tex-math></inline-formula>\u0000 better throughput, 47% shorter average kernel execution time, and 63% shorter tail latency than prior work.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"16 4","pages":"421-424"},"PeriodicalIF":1.7,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139954771","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

Controlling a House’s Air-Conditioning Using Nonlinear Model Predictive Control 利用非线性模型预测控制来控制房屋空调系统

IF 1.6 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2024-01-04 DOI: 10.1109/LES.2023.3348705

Bashra Kadhim Oleiwi;Ahmad H. Sabry

引用次数: 0

DATA: Throughput and Deadline-Aware Genetic Approach for Task Scheduling in Fog Networks DATA：用于雾网络任务调度的吞吐量和截止时间感知遗传方法

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2023-12-29 DOI: 10.1109/LES.2023.3348499

Arya Motamedhashemi;Bardia Safaei;Amir Mahdi Hosseini Monazzah;Alireza Ejlali

{"title":"DATA: Throughput and Deadline-Aware Genetic Approach for Task Scheduling in Fog Networks","authors":"Arya Motamedhashemi;Bardia Safaei;Amir Mahdi Hosseini Monazzah;Alireza Ejlali","doi":"10.1109/LES.2023.3348499","DOIUrl":"10.1109/LES.2023.3348499","url":null,"abstract":"Fog devices in fog computing frameworks are responsible for fetching and executing the tasks submitted by the deployed resource-constraint embedded edge devices. Based on the availability of resources, tasks are offloaded to the virtual machines hosted by the fog devices. These tasks may then get scheduled to guarantee a number of efficiency-related metrics. While throughput has a decisive impact on the timely execution of tasks, the appropriate utilization of this metric has not been considered in the existing mechanisms. In this letter, we first discuss the proper use of this objective in the fitness function of meta-heuristic algorithms. Then, we explain that adopting throughput by the fitness functions in the form of two conventionally used weighted-sum, and fractional techniques may ignore solutions with a better guarantee ratio. Consequently, we propose a novel approach called DATA to be replaced with these two old approaches. DATA is a throughput, and deadline-aware task scheduling mechanism for time-sensitive fog frameworks, which its fitness function utilizes genetic optimization by encoding the solutions into chromosomes. It uses single-gene mutation and two-point crossover. In this approach, two populations are considered to search the problem space. The main population is evaluated based on the guarantee ratio, while the helper population is evaluated based on the throughput. Furthermore, the helper population uses weighted-sum. The initial population is generated randomly by the uniform distribution, to provide a load-balancing. Based on our extensive evaluations, the selected solution by DATA provides the highest guarantee ratio, while having the lowest possible makespan.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"16 4","pages":"409-412"},"PeriodicalIF":1.7,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199417","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

HTree: Hardware Trojan Attack on Cache Resizing Policies HTree：针对缓存大小调整策略的硬件木马攻击

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2023-12-27 DOI: 10.1109/LES.2023.3347607

Atul Kumar;Shirshendu Das;Basant Subba

引用次数: 0

A Low-Cost Embedded System to Support Broadcasting Emergency Messages Through FM Radio Stations 支持通过调频广播电台播放紧急信息的低成本嵌入式系统

IF 1.7 4区计算机科学

IEEE Embedded Systems Letters Pub Date : 2023-12-15 DOI: 10.1109/LES.2023.3343641

M. Coelho;L. Santiago;D. Araújo;A. Navarro;N. B. Carvalho

引用次数: 0