Accelerators for Video Analytics****

视频分析加速器****

• 批准号: 535932-2018
• 负责人: Zilic, Zeljko
• 金额: $ 1.82万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=669937
• 关键词: Accelerators; Video; Analytics

This project will explore the architecture of the machine intelligence accelerators (MIAs), with emphasis on the deep learning from video streams, leading to accelerated video analytics. Among the solutions for the core inference steps from video streams, we will explore the use of sparsity, arithmetic precision, as well as the nonstandard arithmetic encodings. Detailed performance evaluation with respect to throughput, circuit complexity and power, as well as the learning tasks, will be performed over multiple candidate solutions, and the architecture tuned for video analytics presentation will be implemented in Field-Programmable Gate Arrays. The performance of the selected candidate solutions will be evaluated against the realistic, large-scale real-life video streams that represent the typical benchmarks of the industrial partner, TwentyBN. The typical workloads of the software implementations by TwentyBN will be used for evaluating the performance of the overall solution. ****

该项目将探索机器智能加速器（MIA）的体系结构，重点是从视频流进行深入学习，从而导致了加速的视频分析。在视频流的核心推理步骤的解决方案中，我们将探索稀疏性，算术精度以及非标准算术编码的使用。关于吞吐量，电路复杂性和功率以及学习任务的详细绩效评估将对多个候选解决方案进行，并且调整了视频分析演示的体系结构将在现场编程的门阵列中实现。选定的候选解决方案的性能将根据逼真的大规模现实生活视频流进行评估，该视频流代表工业合作伙伴Twentybn的典型基准。 TwentyBN的软件实现的典型工作负载将用于评估整体解决方案的性能。 ****