Accelerators for Video Analytics****

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。关于吞吐量、电路复杂性和功耗以及学习任务的详细性能评估将在多个候选解决方案上执行，并且为视频分析演示调整的架构将在现场可编程门阵列中实现。所选候选解决方案的性能将根据代表工业合作伙伴TwentyBN的典型基准的现实、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个解决方案的性能。* * * *

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. ****