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）的架构，重点是从视频流中进行深度学习，从而加速视频分析。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。详细的性能评估方面的吞吐量，电路复杂性和功率，以及学习任务，将在多个候选解决方案，并调整视频分析演示的架构将在现场可编程门阵列实现。所选候选解决方案的性能将根据代表工业合作伙伴TwentyBN的典型基准的逼真的大规模现实视频流进行评估。TwentyBN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。****