QUEST: A 7.49TOPS multi-purpose log-quantized DNN inference engine stacked on 96MB 3D SRAM using inductive-coupling technology in 40nm CMOS

Kodai Ueyoshi, Kota Ando, Kazutoshi Hirose, Shinya Takamaeda-Yamazaki, J. Kadomoto, T. Miyata, M. Hamada, T. Kuroda, M. Motomura
{"title":"QUEST: A 7.49TOPS multi-purpose log-quantized DNN inference engine stacked on 96MB 3D SRAM using inductive-coupling technology in 40nm CMOS","authors":"Kodai Ueyoshi, Kota Ando, Kazutoshi Hirose, Shinya Takamaeda-Yamazaki, J. Kadomoto, T. Miyata, M. Hamada, T. Kuroda, M. Motomura","doi":"10.1109/ISSCC.2018.8310261","DOIUrl":null,"url":null,"abstract":"A key consideration for deep neural network (DNN) inference accelerators is the need for large and high-bandwidth external memories. Although an architectural concept for stacking a DNN accelerator with DRAMs has been proposed previously, long DRAM latency remains problematic and limits the performance [1]. Recent algorithm-level optimizations, such as network pruning and compression, have shown success in reducing the DNN memory size [2]; however, since networks become irregular and sparse, they induce an additional need for agile random accesses to the memory systems.","PeriodicalId":6617,"journal":{"name":"2018 IEEE International Solid - State Circuits Conference - (ISSCC)","volume":"135 1","pages":"216-218"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"62","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Solid - State Circuits Conference - (ISSCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSCC.2018.8310261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 62

Abstract

A key consideration for deep neural network (DNN) inference accelerators is the need for large and high-bandwidth external memories. Although an architectural concept for stacking a DNN accelerator with DRAMs has been proposed previously, long DRAM latency remains problematic and limits the performance [1]. Recent algorithm-level optimizations, such as network pruning and compression, have shown success in reducing the DNN memory size [2]; however, since networks become irregular and sparse, they induce an additional need for agile random accesses to the memory systems.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
QUEST:一个7.49TOPS多用途对数量化DNN推理引擎,采用40nm CMOS电感耦合技术,堆叠在96MB 3D SRAM上
深度神经网络(DNN)推理加速器的一个关键考虑因素是需要大带宽的外部存储器。虽然之前已经提出了将DNN加速器与DRAM堆叠的架构概念,但长DRAM延迟仍然是问题并限制了性能[1]。最近的算法级优化,如网络修剪和压缩,已经成功地减少了DNN内存大小[2];然而,由于网络变得不规则和稀疏,它们引发了对灵活随机访问存储系统的额外需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
EE1: Student research preview (SRP) A 512Gb 3b/Cell 3D flash memory on a 96-word-line-layer technology Single-chip reduced-wire active catheter system with programmable transmit beamforming and receive time-division multiplexing for intracardiac echocardiography A 2.5nJ duty-cycled bridge-to-digital converter integrated in a 13mm3 pressure-sensing system A 36.3-to-38.2GHz −216dBc/Hz2 40nm CMOS fractional-N FMCW chirp synthesizer PLL with a continuous-time bandpass delta-sigma time-to-digital converter
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1