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A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices
Power efficiency is becoming a critical aspect of IoT devices. In this paper, we present a compact object-detection coprocessor with multiple cores for multi-scale/type classification. This coprocessor is capable to process scalable block size for multi-shape detection-window and can be compatible w...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662932/ https://www.ncbi.nlm.nih.gov/pubmed/33142931 http://dx.doi.org/10.3390/s20216239 |
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author | Xu, Peng Xiao, Zhihua Wang, Xianglong Chen, Lei Wang, Chao An, Fengwei |
author_facet | Xu, Peng Xiao, Zhihua Wang, Xianglong Chen, Lei Wang, Chao An, Fengwei |
author_sort | Xu, Peng |
collection | PubMed |
description | Power efficiency is becoming a critical aspect of IoT devices. In this paper, we present a compact object-detection coprocessor with multiple cores for multi-scale/type classification. This coprocessor is capable to process scalable block size for multi-shape detection-window and can be compatible with the frame-image sizes up to 2048 × 2048 for multi-scale classification. A memory-reuse strategy that requires only one dual-port SRAM for storing the feature-vector of one-row blocks is developed to save memory usage. Eventually, a prototype platform is implemented on the Intel DE4 development board with the Stratix IV device. The power consumption of each core in FPGA is only 80.98 mW. |
format | Online Article Text |
id | pubmed-7662932 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76629322020-11-14 A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices Xu, Peng Xiao, Zhihua Wang, Xianglong Chen, Lei Wang, Chao An, Fengwei Sensors (Basel) Article Power efficiency is becoming a critical aspect of IoT devices. In this paper, we present a compact object-detection coprocessor with multiple cores for multi-scale/type classification. This coprocessor is capable to process scalable block size for multi-shape detection-window and can be compatible with the frame-image sizes up to 2048 × 2048 for multi-scale classification. A memory-reuse strategy that requires only one dual-port SRAM for storing the feature-vector of one-row blocks is developed to save memory usage. Eventually, a prototype platform is implemented on the Intel DE4 development board with the Stratix IV device. The power consumption of each core in FPGA is only 80.98 mW. MDPI 2020-10-31 /pmc/articles/PMC7662932/ /pubmed/33142931 http://dx.doi.org/10.3390/s20216239 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xu, Peng Xiao, Zhihua Wang, Xianglong Chen, Lei Wang, Chao An, Fengwei A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices |
title | A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices |
title_full | A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices |
title_fullStr | A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices |
title_full_unstemmed | A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices |
title_short | A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices |
title_sort | multi-core object detection coprocessor for multi-scale/type classification applicable to iot devices |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662932/ https://www.ncbi.nlm.nih.gov/pubmed/33142931 http://dx.doi.org/10.3390/s20216239 |
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