面向人工智能应用的高密度、低功耗内存积和计算的新型3D and型NVM架构

H. Lue, Wei-Chen Chen, Hung-Sheng Chang, Keh-Chung Wang, Chih-Yuan Lu
{"title":"面向人工智能应用的高密度、低功耗内存积和计算的新型3D and型NVM架构","authors":"H. Lue, Wei-Chen Chen, Hung-Sheng Chang, Keh-Chung Wang, Chih-Yuan Lu","doi":"10.1109/VLSIT.2018.8510688","DOIUrl":null,"url":null,"abstract":"An AND-type stackable 3D NVM architecture is proposed to provide an ultra-high density AI computing memory with low power. The advantages are: (1) All memory transistors in the 3D array are connected in parallel, thus enable the sum-of-product operation. (2) The 3D NAND like architecture is possible to stack to > 64 layers, thus provides ultra-high density (>128Gb) AI memory. (3) Many bit lines (>1KB) can operate in parallel for high bandwidth. (4) Uses low-power +/− FN programming/erasing which allows high parallelism, and is bit-alterable thus is ideal for training or transfer learning. (5) Excellent linearity of output current with respect to bitline bias, thus enabling ideal analog computation. (6) Adequate sensing current of the summed product thus permits fast access read for inference device. The proposed memory architecture can achieve TOPS/W>10, which is 10X greater than the conventional von Neumann architecture.","PeriodicalId":6561,"journal":{"name":"2018 IEEE Symposium on VLSI Technology","volume":"9 1","pages":"177-178"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"A Novel 3D AND-type NVM Architecture Capable of High-density, Low-power In-Memory Sum-of-Product Computation for Artificial Intelligence Application\",\"authors\":\"H. Lue, Wei-Chen Chen, Hung-Sheng Chang, Keh-Chung Wang, Chih-Yuan Lu\",\"doi\":\"10.1109/VLSIT.2018.8510688\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An AND-type stackable 3D NVM architecture is proposed to provide an ultra-high density AI computing memory with low power. The advantages are: (1) All memory transistors in the 3D array are connected in parallel, thus enable the sum-of-product operation. (2) The 3D NAND like architecture is possible to stack to > 64 layers, thus provides ultra-high density (>128Gb) AI memory. (3) Many bit lines (>1KB) can operate in parallel for high bandwidth. (4) Uses low-power +/− FN programming/erasing which allows high parallelism, and is bit-alterable thus is ideal for training or transfer learning. (5) Excellent linearity of output current with respect to bitline bias, thus enabling ideal analog computation. (6) Adequate sensing current of the summed product thus permits fast access read for inference device. The proposed memory architecture can achieve TOPS/W>10, which is 10X greater than the conventional von Neumann architecture.\",\"PeriodicalId\":6561,\"journal\":{\"name\":\"2018 IEEE Symposium on VLSI Technology\",\"volume\":\"9 1\",\"pages\":\"177-178\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Symposium on VLSI Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VLSIT.2018.8510688\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Symposium on VLSI Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSIT.2018.8510688","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19

摘要

为了提供一种低功耗的超高密度AI计算存储器,提出了一种and型可堆叠3D NVM架构。其优点是:(1)3D阵列中所有的存储晶体管都是并联的,因此可以进行和积运算。(2)类似3D NAND的架构可以堆叠到> 64层,从而提供超高密度(>128Gb)的AI内存。(3)多个位线(>1KB)可以并行工作,以获得高带宽。(4)使用低功耗+/−FN编程/擦除,允许高并行性,并且是位可变的,因此是理想的训练或迁移学习。(5)输出电流相对于位线偏置的良好线性,从而实现理想的模拟计算。(6)因此,所述总和产品有足够的感应电流,可以为推理装置提供快速读取。所提出的存储器结构可以达到TOPS/W>10,是传统冯·诺依曼结构的10倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
A Novel 3D AND-type NVM Architecture Capable of High-density, Low-power In-Memory Sum-of-Product Computation for Artificial Intelligence Application
An AND-type stackable 3D NVM architecture is proposed to provide an ultra-high density AI computing memory with low power. The advantages are: (1) All memory transistors in the 3D array are connected in parallel, thus enable the sum-of-product operation. (2) The 3D NAND like architecture is possible to stack to > 64 layers, thus provides ultra-high density (>128Gb) AI memory. (3) Many bit lines (>1KB) can operate in parallel for high bandwidth. (4) Uses low-power +/− FN programming/erasing which allows high parallelism, and is bit-alterable thus is ideal for training or transfer learning. (5) Excellent linearity of output current with respect to bitline bias, thus enabling ideal analog computation. (6) Adequate sensing current of the summed product thus permits fast access read for inference device. The proposed memory architecture can achieve TOPS/W>10, which is 10X greater than the conventional von Neumann architecture.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Low RA Magnetic Tunnel Junction Arrays in Conjunction with Low Switching Current and High Breakdown Voltage for STT-MRAM at 10 nm and Beyond A Circuit Compatible Accurate Compact Model for Ferroelectric-FETs A Threshold Switch Augmented Hybrid-FeFET (H-FeFET) with Enhanced Read Distinguishability and Reduced Programming Voltage for Non-Volatile Memory Applications Sensors and related devices for IoT, medicine and s mart-living A Comprehensive Study of Polymorphic Phase Distribution of Ferroelectric-Dielectrics and Interfacial Layer Effects on Negative Capacitance FETs for Sub-5 nm Node
×
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