MN-Core - A Highly Efficient and Scalable Approach to Deep Learning

Ken Namura, Johannes Maximilian Kühn, T. Adachi, H. Imachi, H. Kaneko, T. Kato, Go Watanabe, Naoto Tanaka, S. Kashihara, Hiroshi Miyashita, Y. Tomonaga, Ryosuke Okuta, Takuya Akiba, Brian K. Vogel, S. Kitajo, F. Osawa, K. Takahashi, Y. Takatsukasa, K. Mizumaru, T. Yamauchi, J. Ono, A. Takahashi, Tanvir Ahmed, Y. Doi, K. Hiraki, J. Makino
{"title":"MN-Core - A Highly Efficient and Scalable Approach to Deep Learning","authors":"Ken Namura, Johannes Maximilian Kühn, T. Adachi, H. Imachi, H. Kaneko, T. Kato, Go Watanabe, Naoto Tanaka, S. Kashihara, Hiroshi Miyashita, Y. Tomonaga, Ryosuke Okuta, Takuya Akiba, Brian K. Vogel, S. Kitajo, F. Osawa, K. Takahashi, Y. Takatsukasa, K. Mizumaru, T. Yamauchi, J. Ono, A. Takahashi, Tanvir Ahmed, Y. Doi, K. Hiraki, J. Makino","doi":"10.23919/VLSICircuits52068.2021.9492395","DOIUrl":null,"url":null,"abstract":"MN-Core is a highly efficient deep learning training accelerator reaching in excess of 1 TFLOPS/W (half-precision) at board level in real-world mixed-precision workloads. To reach and sustain this level of performance, the design is partitioned and packaged as four-die MCM package exceeding 3000mm2 of die area.","PeriodicalId":106356,"journal":{"name":"2021 Symposium on VLSI Circuits","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Symposium on VLSI Circuits","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSICircuits52068.2021.9492395","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

MN-Core is a highly efficient deep learning training accelerator reaching in excess of 1 TFLOPS/W (half-precision) at board level in real-world mixed-precision workloads. To reach and sustain this level of performance, the design is partitioned and packaged as four-die MCM package exceeding 3000mm2 of die area.
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
n - core -一种高效和可扩展的深度学习方法
MN-Core是一款高效的深度学习训练加速器,在实际的混合精度工作负载中,在板级达到超过1 TFLOPS/W(半精度)。为了达到并维持这一性能水平,该设计被分割并封装为超过3000mm2的四模MCM封装。
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
PIMCA: A 3.4-Mb Programmable In-Memory Computing Accelerator in 28nm for On-Chip DNN Inference A 24–31 GHz Reference Oversampling ADPLL Achieving FoMjitter−N of -269.3 dB A 6.78 MHz Wireless Power Transfer System for Simultaneous Charging of Multiple Receivers with Maximum Efficiency using Adaptive Magnetic Field Distributor IC Enhanced Core Circuits for scaling DRAM: 0.7V VCC with Long Retention 138ms at 125°C and Random Row/Column Access Times Accelerated by 1.5ns A Sub-mW Dual-Engine ML Inference System-on-Chip for Complete End-to-End Face-Analysis at the Edge
×
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