A comprehensive analysis of DAC-SDC FPGA low power object detection challenge

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Science China Information Sciences Pub Date : 2024-07-24 DOI:10.1007/s11432-023-3958-4
Jingwei Zhang, Guoqing Li, Meng Zhang, Xinye Cao, Yu Zhang, Xiang Li, Ziyang Chen, Jun Yang
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Abstract

The lower power object detection challenge (LPODC) at the IEEE/ACM Design Automation Conference is a premier contest in low-power object detection and algorithm (software)-hardware co-design for edge artificial intelligence, which has been a success in the past five years. LPODC focused on designing and implementing novel algorithms on the edge platform for object detection in images taken from unmanned aerial vehicles (UAVs), which attracted hundreds of teams from dozens of countries to participate. Our team SEUer has been participating in this competition for three consecutive years from 2020 to 2022 and obtained sixth place respectively in 2020 and 2021. Recently, we achieved the championship in 2022. In this paper, we presented the LPODC for UAV object detection from 2018 to 2022, including the dataset, hardware platform, and evaluation method. In addition, we also introduced and discussed the details of methods proposed by each year’s top three teams from 2018 to 2022 in terms of network, accuracy, quantization method, hardware performance, and total score. Additionally, we conducted an in-depth analysis of the selected entries and results, along with summarizing representative methodologies. This analysis serves as a valuable practical resource for researchers and engineers in deploying the UAV application on edge platforms and enhancing its feasibility and reliability. According to the analysis and discussion, it becomes evident that the adoption of a hardware-algorithm co-design approach is paramount in the context of tiny machine learning (TinyML). This approach surpasses the mere optimization of software and hardware as separate entities, proving to be essential for achieving optimal performance and efficiency in TinyML applications.

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全面分析 DAC-SDC FPGA 的低功耗物体检测挑战
IEEE/ACM 设计自动化大会的低功耗物体检测挑战赛(LPODC)是低功耗物体检测和边缘人工智能算法(软件)-硬件协同设计领域的顶级竞赛,在过去五年中取得了巨大成功。LPODC 重点关注在边缘平台上设计和实现新型算法,用于无人机(UAV)拍摄的图像中的物体检测,吸引了来自数十个国家的数百个团队参赛。我校 SEUer 团队自 2020 年至 2022 年连续三年参加该竞赛,并分别于 2020 年和 2021 年获得第六名。最近,我们又在 2022 年取得了冠军。在本文中,我们介绍了2018年至2022年用于无人机物体检测的LPODC,包括数据集、硬件平台和评估方法。此外,我们还从网络、精度、量化方法、硬件性能、总分等方面介绍和讨论了 2018 年至 2022 年每年前三名团队提出的方法细节。此外,我们还对入选作品和结果进行了深入分析,同时总结了具有代表性的方法。该分析为研究人员和工程师在边缘平台上部署无人机应用、提高其可行性和可靠性提供了宝贵的实用资源。根据分析和讨论,可以明显看出,在微型机器学习(TinyML)的背景下,采用硬件-算法协同设计方法至关重要。这种方法超越了单纯将软件和硬件作为独立实体进行优化的做法,对实现 TinyML 应用的最佳性能和效率至关重要。
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来源期刊
Science China Information Sciences
Science China Information Sciences COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
12.60
自引率
5.70%
发文量
224
审稿时长
8.3 months
期刊介绍: Science China Information Sciences is a dedicated journal that showcases high-quality, original research across various domains of information sciences. It encompasses Computer Science & Technologies, Control Science & Engineering, Information & Communication Engineering, Microelectronics & Solid-State Electronics, and Quantum Information, providing a platform for the dissemination of significant contributions in these fields.
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