Pub Date : 2020-12-08DOI: 10.1109/APCCAS50809.2020.9301685
Jinghao Ye, M. Yanagisawa, Youhua Shi
In this paper, a symmetric hybrid form for high performance finite impulse response (FIR) filters with symmetric coefficients is proposed, which can be utilized in both fixed and reconfigurable FIR implementations to solve the driving capacity problem caused by the high fanout signals in the existing symmetric transposed form based FIR architecture. The evaluation results show that, when compared with the existing high speed FIR designs such as the symmetric systolic form in [13] and the hybrid form in [1], the proposed form can achieve significant area and power savings with great ADP and PDP reduction. Moreover, when compared with the symmetric systolic form in [13] the required latency can be approximately reduced by 33.3%, which clearly shows the performance improvement of the proposed method.
{"title":"A High-Performance Symmetric Hybrid Form Design for High-Order FIR Filters","authors":"Jinghao Ye, M. Yanagisawa, Youhua Shi","doi":"10.1109/APCCAS50809.2020.9301685","DOIUrl":"https://doi.org/10.1109/APCCAS50809.2020.9301685","url":null,"abstract":"In this paper, a symmetric hybrid form for high performance finite impulse response (FIR) filters with symmetric coefficients is proposed, which can be utilized in both fixed and reconfigurable FIR implementations to solve the driving capacity problem caused by the high fanout signals in the existing symmetric transposed form based FIR architecture. The evaluation results show that, when compared with the existing high speed FIR designs such as the symmetric systolic form in [13] and the hybrid form in [1], the proposed form can achieve significant area and power savings with great ADP and PDP reduction. Moreover, when compared with the symmetric systolic form in [13] the required latency can be approximately reduced by 33.3%, which clearly shows the performance improvement of the proposed method.","PeriodicalId":127075,"journal":{"name":"2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127966255","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}
Pub Date : 2020-12-08DOI: 10.1109/APCCAS50809.2020.9301706
T. Teo, Y. Tan
Autonomous vehicles using Artificial Intelligence (AI) technologies requires various sensors such as radars, lidar, ultrasonic, and etc. to mimic the human visual perception in monitoring the road condition. Wide-angle camera is also often adopted for better coverage of view. Those sensors generates massive amount of data that could be processed with the cloud computing through the wireless communication. However, the cloud computing may not be a feasible solution, such as for real-time detection systems. In this work, we examine the implementation of the deep-learning (DL) real-time object detection models on the edge devices that is connected to the wide-angle camera. This visual system can achieve real-time object detection with a latency of less than 0.2 ms. The DL model also help to mitigate the distortion that is introduced by the wide-angle camera. Such a detection system will be able to warn the user of his or her surrounding road conditions.
{"title":"Fast Object Detection on the Road","authors":"T. Teo, Y. Tan","doi":"10.1109/APCCAS50809.2020.9301706","DOIUrl":"https://doi.org/10.1109/APCCAS50809.2020.9301706","url":null,"abstract":"Autonomous vehicles using Artificial Intelligence (AI) technologies requires various sensors such as radars, lidar, ultrasonic, and etc. to mimic the human visual perception in monitoring the road condition. Wide-angle camera is also often adopted for better coverage of view. Those sensors generates massive amount of data that could be processed with the cloud computing through the wireless communication. However, the cloud computing may not be a feasible solution, such as for real-time detection systems. In this work, we examine the implementation of the deep-learning (DL) real-time object detection models on the edge devices that is connected to the wide-angle camera. This visual system can achieve real-time object detection with a latency of less than 0.2 ms. The DL model also help to mitigate the distortion that is introduced by the wide-angle camera. Such a detection system will be able to warn the user of his or her surrounding road conditions.","PeriodicalId":127075,"journal":{"name":"2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128108374","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}
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