Embedded Compression for Embedded Intelligent Vision & Video System

目前，视频编码和计算机视觉算法被广泛应用于移动设备，如智能手机和无线传感器网络。虽然这些设备中有许多是电池供电的，甚至没有电池，但低能耗是至关重要的。在这项研究中，我的重点是通过嵌入式压缩来降低DRAM访问的主要能耗。DRAM访问功率与其体积成正比，因此可以通过在将数据存储到DRAM之前压缩数据并在提取数据后解压缩数据来降低功耗。因此，我对嵌入式视频和视觉系统的嵌入式压缩进行了研究。在这一年里，我主要研究基于修剪、量化和编码的深度卷积神经网络的缩减，并提交了一篇期刊论文。

Nowadays, video coding and computer vision algorithms are widely applied in mobile devices, such as smartphones and wireless sensor networks. While many of these devices are battery powered or even battery-less, a low energy consumption is crucial. In this research, I focus on reducing the dominant energy consumption of DRAM access by embedded compression.DRAM access power is proportional to its volume, so it can be reduced by compressing data before storing them to DRAM and decompressing data after fetched back. Hence, I researched on the embedded compression for embedded video and vision systems.In this year, I mainly focus on reducing the size of deep convolutional neural networks based on pruning, quantization and encoding, and submitted one journal paper.