2018 Fiscal Year Annual Research Report

Embedded Compression for Embedded Intelligent Vision & Video System

Research Project

Project/Area Number	17J10477
Research Institution	Waseda University
Principal Investigator	郭栗早稲田大学, 情報生産システム研究科, 特別研究員(DC2)
Project Period (FY)	2017-04-26 – 2019-03-31
Keywords	embedded compression / power consumption / neural network
Outline of Annual Research Achievements	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.
Research Progress Status	平成30年度が最終年度であるため、記入しない。
Strategy for Future Research Activity	平成30年度が最終年度であるため、記入しない。