2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)最新文献_第3页

A Simple Monitor for Tracking NBTI in Integrated Systems 一种用于跟踪集成系统中NBTI的简单监视器

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531715

M. Strong, Kushagra Bhatheja, Ruohan Yang, Degang Chen

引用次数: 0

Efficient Hardware Implementation of Cube Architecture using Yavadunam Sutra on FPGA 基于Yavadunam经的立方体架构在FPGA上的高效硬件实现

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531843

M. Thakare, Pal Yash, D. Chakraborty, Babita Jajodia

引用次数: 1

Threshold Detection ADC For Continuous Monitoring Applications 用于连续监控应用的阈值检测ADC

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531751

Annamaria Fordymacka, I. O'Connell

引用次数: 0

A Reversible-Logic based Architecture for Convolutional Neural Network (CNN) 基于可逆逻辑的卷积神经网络(CNN)架构

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531842

Kasem Khalil, Bappaditya Dey, Ashok Kumar V, M. Bayoumi

{"title":"A Reversible-Logic based Architecture for Convolutional Neural Network (CNN)","authors":"Kasem Khalil, Bappaditya Dey, Ashok Kumar V, M. Bayoumi","doi":"10.1109/MWSCAS47672.2021.9531842","DOIUrl":"https://doi.org/10.1109/MWSCAS47672.2021.9531842","url":null,"abstract":"Convolutional-Neural-Network (CNN) is a deep learning model, which is used extensively to solve complex image classification or computer vision problems. CNN and more complex architecture variants of it such as vggX, GoogleNet, ImageNet, etc. are widely used in various application domains such as object detection, self-driving cars, instance segmentation, Optical Character Recognition (OCR), surveillance and security systems, etc. However, operations involved under CNN are both computationally as well as memory extensive which further leads to high computational cost, area overhead, and excessive power dissipation against higher accuracy compatible architectures discussed above. In this paper, we have proposed a novel design of fully reversible-logic-based CNN architecture in the context of low-power VLSI (Very-Large-Scale-Integration) circuit synthesis. Ideally, reversible logic operations are lossless due to no information-loss mechanism, which results in Zero-heat dissipation. The proposed architecture has been implemented using VHDL on Altera Arria10 GX FPGA. The comparative analysis demonstrates that the proposed approach has achieved an approximately 19.24% decrease in overall power dissipation compared to the conventional classical approach. The proposed approach also has better scalability than the classical design approach.","PeriodicalId":6792,"journal":{"name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"25 1","pages":"1070-1073"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83384781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparison between Class-E DC-DC Design Methodologies for Wireless Power Transfer 无线电力传输e类DC-DC设计方法的比较

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531712

A. Celentano, Fabio Pareschi, V. Valente, R. Rovatti, W. Serdijn, G. Setti

引用次数: 0

An Efficient Implementation of FPGA-based Object Detection Using Multi-scale Attention 基于fpga的多尺度注意力目标检测的高效实现

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531732

M. Furuta, K. Ban, Daisuke Kobayashi, Tomoyuki Shibata

{"title":"An Efficient Implementation of FPGA-based Object Detection Using Multi-scale Attention","authors":"M. Furuta, K. Ban, Daisuke Kobayashi, Tomoyuki Shibata","doi":"10.1109/MWSCAS47672.2021.9531732","DOIUrl":"https://doi.org/10.1109/MWSCAS47672.2021.9531732","url":null,"abstract":"Convolutional neural network (CNN)-based object detection has become an important task in image pre-processing for security video surveillance cameras. Since CNNs require a large amount of computational power, the approach of FPGA-based implementation has emerged as a promising solution owing to its energy efficiency and processing speed. The single-shot multibox detector (SSD) is suitable for this kind of application, while reducing the CNN computational load need to achieve high detection accuracy is still an important challenge for FPGA design. This paper presents an FPGA accelerator for processing SSD object detection. The number of computations can be reduced by the proposed multi-scale spatial attention mechanism. To enhance the efficiency for hardware implementation of the CNN, we propose dynamic quantization on hardware and autonomous memory access control. The developed prototype based on SSD with multi-scale spatial attention mechanism implemented on XCZU7EV exhibits an object detection accuracy of 79.3% mean average precision on the PASCAL VOC dataset. The proposed design achieves high digital signal processor efficiency of 94% and good operation speed of 77.7 msec.","PeriodicalId":6792,"journal":{"name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"8 1","pages":"321-325"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79571329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Low-Noise Stacked Differential Optical Receiver in 0.18-μm CMOS 一种0.18-μm CMOS低噪声堆叠差分光接收机

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531855

Wei Huang, Xiangwen Liu, Yongjun Shi, Dan Li, Bing Zhang, Xiaoyan Gui, Li Geng

引用次数: 0

A 98.1-dB SNDR 188-dB FoMS Noise-Shaping SAR ADC Using Series Connection Capacitors 一种采用串联电容的98.1 db信噪比188db噪声整形SAR ADC

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531721

Xixin Jing, Zhechong Lan, Bing Zhang, Li Dong, Youze Xin, Zhuoqi Guo, Zhongming Xue, Li Geng

引用次数: 0

Integrated Potentiostat Design for Neurotransmitter Detection in Wireless Implants 无线植入物神经递质检测的集成恒电位器设计

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531719

S. Yilmaz, T. Constandinou, S. Carrara

引用次数: 1

Deep Learning Based Time-Series Forecasting Framework for Olive Precision Farming 基于深度学习的橄榄精准种植时间序列预测框架

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) Pub Date : 2021-08-09 DOI: 10.1109/MWSCAS47672.2021.9531929

M. Atef, Ahmed M. Khattab, E. Agamy, M. Khairy

{"title":"Deep Learning Based Time-Series Forecasting Framework for Olive Precision Farming","authors":"M. Atef, Ahmed M. Khattab, E. Agamy, M. Khairy","doi":"10.1109/MWSCAS47672.2021.9531929","DOIUrl":"https://doi.org/10.1109/MWSCAS47672.2021.9531929","url":null,"abstract":"In this paper, we present a time series forecasting framework that uses deep learning to predict the environmental attributes that affect the olive fruit farming. The proposed framework is composed of two phases: a data preprocessing phase and a prediction phase. In the prediction phase, we use both the Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning approaches to develop two models for predicting the environmental attributes. We evaluate the performance of the framework using real-life agriculture data collected for twenty years from a Spanish olive grove. Our results show that proposed LSTM and GRU models achieve remarkable accuracy, measured through different metrics, in predicting the temperature and relative humidity for the upcoming year based on historical data. We further use the predicted temperature in calculating the degree-day metric used to define the development phases of the olive fruit fly. This allows for foreseeing the best times to apply the counter measures to prevent the outbreak of such a fatal pest that affects a major Mediterranean crop: olive.","PeriodicalId":6792,"journal":{"name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"232 1","pages":"1062-1065"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76903468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1