Optimizing Hardware-Friendly Object Detection Network for Edge Devices

• Item type: Symposium(1)
• 公開日: 2023-08-23
• タイトル: Optimizing Hardware-Friendly Object Detection Network for Edge Devices
• タイトル / 言語: en
• タイトル: Optimizing Hardware-Friendly Object Detection Network for Edge Devices
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: ポスター
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: Waseda University
• 著者所属: Waseda University
• 著者所属: Waseda University
• 著者所属: Waseda University
• 著者所属: Waseda University
• 著者所属(英): en
• 著者所属(英): Waseda University
• 著者所属(英): en
• 著者所属(英): Waseda University
• 著者所属(英): en
• 著者所属(英): Waseda University
• 著者所属(英): en
• 著者所属(英): Waseda University
• 著者所属(英): en
• 著者所属(英): Waseda University
• 著者名: Zeqiu, Liu; Xingan, Sha; Haoyang, Tao; Masao, Yanagisawa; Youhua, Shi
• 著者名(英): Zeqiu, Liu; Xingan, Sha; Haoyang, Tao; Masao, Yanagisawa; Youhua, Shi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Object detection has been a consistently popular research area, but achieving higher performance often necessitates complex structures that rely on powerful hardware. However, deploying such high-performance hardware on edge systems is challenging due to its high cost and power consumption. Conversely, simpler structures may lack accuracy, especially when edge hardware requires quantized models, resulting in a significant performance decline. To address these hardware-related challenges, we focus on developing an optimized structure for object detection on edge devices. We examine the dilemma of structure design and the quantization process for object detection, as well as advancements in re-parameterized structures. In order to optimize the model, we modify the original YOLOv6n structure to reduce hardware resource consumption and improve inference speed. We conduct a novel method QARep for re-parameterization, naming the new model QR-YOLOv6n. The results demonstrate that the QR-YOLOv6n retains its quantization-friendly characteristics, with its INT6 version with only 3.1M parameters achieving 81% mAP(mean Average Precision) on Pascal VOC 2007 dataset and 2.74ms latency on customized FPGA, which is acceptable in terms of performance. The evaluation on a customized dataset also reaches a satisfied mAP. With its low latency, high accuracy, and hardware-friendly structure, QR-YOLOv6n presents a potential solution for edge devices, enabling object detection with low power consumption and high accuracy. This research aims to reduce the cost of AI applications and facilitate the widespread deployment of AI in edge systems.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Object detection has been a consistently popular research area, but achieving higher performance often necessitates complex structures that rely on powerful hardware. However, deploying such high-performance hardware on edge systems is challenging due to its high cost and power consumption. Conversely, simpler structures may lack accuracy, especially when edge hardware requires quantized models, resulting in a significant performance decline. To address these hardware-related challenges, we focus on developing an optimized structure for object detection on edge devices. We examine the dilemma of structure design and the quantization process for object detection, as well as advancements in re-parameterized structures. In order to optimize the model, we modify the original YOLOv6n structure to reduce hardware resource consumption and improve inference speed. We conduct a novel method QARep for re-parameterization, naming the new model QR-YOLOv6n. The results demonstrate that the QR-YOLOv6n retains its quantization-friendly characteristics, with its INT6 version with only 3.1M parameters achieving 81% mAP(mean Average Precision) on Pascal VOC 2007 dataset and 2.74ms latency on customized FPGA, which is acceptable in terms of performance. The evaluation on a customized dataset also reaches a satisfied mAP. With its low latency, high accuracy, and hardware-friendly structure, QR-YOLOv6n presents a potential solution for edge devices, enabling object detection with low power consumption and high accuracy. This research aims to reduce the cost of AI applications and facilitate the widespread deployment of AI in edge systems.
• 書誌情報: DAシンポジウム2023論文集; 巻 2023, p. 124-128, 発行日 2023-08-23
• 出版者 / 言語: ja
• 出版者: 情報処理学会