Kota Ando, Kodai Ueyoshi, Kentaro Orimo, H. Yonekawa, Shimpei Sato, Hiroki Nakahara, M. Ikebe, T. Asai, Shinya Takamaeda-Yamazaki, T. Kuroda, M. Motomura
{"title":"BRein memory: A 13-layer 4.2 K neuron/0.8 M synapse binary/ternary reconfigurable in-memory deep neural network accelerator in 65 nm CMOS","authors":"Kota Ando, Kodai Ueyoshi, Kentaro Orimo, H. Yonekawa, Shimpei Sato, Hiroki Nakahara, M. Ikebe, T. Asai, Shinya Takamaeda-Yamazaki, T. Kuroda, M. Motomura","doi":"10.23919/VLSIC.2017.8008533","DOIUrl":null,"url":null,"abstract":"A versatile reconfigurable accelerator for binary/ternary deep neural networks (DNNs) is presented. It features a massively parallel in-memory processing architecture and stores varieties of binary/ternary DNNs with a maximum of 13 layers, 4.2 K neurons, and 0.8 M synapses on chip. The 0.6 W, 1.4 TOPS chip achieves performance and energy efficiency that is 10–10<sup>2</sup> and 10<sup>2</sup>–10<sup>4</sup> times better than a CPU/GPU/FPGA.","PeriodicalId":176340,"journal":{"name":"2017 Symposium on VLSI Circuits","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"72","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Symposium on VLSI Circuits","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSIC.2017.8008533","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 72
Abstract
A versatile reconfigurable accelerator for binary/ternary deep neural networks (DNNs) is presented. It features a massively parallel in-memory processing architecture and stores varieties of binary/ternary DNNs with a maximum of 13 layers, 4.2 K neurons, and 0.8 M synapses on chip. The 0.6 W, 1.4 TOPS chip achieves performance and energy efficiency that is 10–102 and 102–104 times better than a CPU/GPU/FPGA.