波尔兹曼机的精确硬件实现

Marcin Skubiszewski
{"title":"波尔兹曼机的精确硬件实现","authors":"Marcin Skubiszewski","doi":"10.1109/SPDP.1992.242756","DOIUrl":null,"url":null,"abstract":"The author presents a faithful hardware implementation (built on the top of DECPeRLe-1, a reconfigurable coprocessor closely coupled with its host machine, a DECstation 500) of the Boltzmann machine. The prototype performs 505 megasynapses (million of additions and multiplications) per second, using 16-b fixed-point weights. It can emulate fully connected instances of the Boltzmann machine containing up to 1438 variables. This specialized hardware only executes the simplest part of the Boltzmann machine algorithm, namely, multiplying matrices of numbers by vectors of bits. The other operations (which are complicated, but only require a modest amount of computation) are performed by the host processor. It is noted that the key point of this work resides in establishing the right design choices. Among these, the most important ones are the rejection of 'neural parallelism', which makes the implementation exact, and the algorithm used to generate random numbers in software, which allows the hardware to be simple. The fact that DECPeRLe-1 makes hardware development cheap and fast was essential in this work.<<ETX>>","PeriodicalId":265469,"journal":{"name":"[1992] Proceedings of the Fourth IEEE Symposium on Parallel and Distributed Processing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"An exact hardware implementation of the Boltzmann machine\",\"authors\":\"Marcin Skubiszewski\",\"doi\":\"10.1109/SPDP.1992.242756\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The author presents a faithful hardware implementation (built on the top of DECPeRLe-1, a reconfigurable coprocessor closely coupled with its host machine, a DECstation 500) of the Boltzmann machine. The prototype performs 505 megasynapses (million of additions and multiplications) per second, using 16-b fixed-point weights. It can emulate fully connected instances of the Boltzmann machine containing up to 1438 variables. This specialized hardware only executes the simplest part of the Boltzmann machine algorithm, namely, multiplying matrices of numbers by vectors of bits. The other operations (which are complicated, but only require a modest amount of computation) are performed by the host processor. It is noted that the key point of this work resides in establishing the right design choices. Among these, the most important ones are the rejection of 'neural parallelism', which makes the implementation exact, and the algorithm used to generate random numbers in software, which allows the hardware to be simple. The fact that DECPeRLe-1 makes hardware development cheap and fast was essential in this work.<<ETX>>\",\"PeriodicalId\":265469,\"journal\":{\"name\":\"[1992] Proceedings of the Fourth IEEE Symposium on Parallel and Distributed Processing\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992] Proceedings of the Fourth IEEE Symposium on Parallel and Distributed Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPDP.1992.242756\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992] Proceedings of the Fourth IEEE Symposium on Parallel and Distributed Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPDP.1992.242756","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

本文给出了玻尔兹曼机的一个忠实的硬件实现(建立在DECPeRLe-1之上,DECPeRLe-1是一个可重构协处理器,与它的主机DECstation 500紧密耦合)。原型每秒执行505个megasynapses(百万次加法和乘法),使用16-b的定点权重。它可以模拟包含多达1438个变量的玻尔兹曼机的完全连接实例。这种专用硬件只执行玻尔兹曼机器算法中最简单的部分,即将数字矩阵乘以位向量。其他操作(比较复杂,但只需要少量的计算)由主机处理器执行。值得注意的是,这项工作的关键点在于建立正确的设计选择。其中,最重要的是拒绝“神经并行”,这使得实现精确,以及在软件中用于生成随机数的算法,这使得硬件变得简单。DECPeRLe-1使硬件开发变得廉价和快速,这一事实对这项工作至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An exact hardware implementation of the Boltzmann machine
The author presents a faithful hardware implementation (built on the top of DECPeRLe-1, a reconfigurable coprocessor closely coupled with its host machine, a DECstation 500) of the Boltzmann machine. The prototype performs 505 megasynapses (million of additions and multiplications) per second, using 16-b fixed-point weights. It can emulate fully connected instances of the Boltzmann machine containing up to 1438 variables. This specialized hardware only executes the simplest part of the Boltzmann machine algorithm, namely, multiplying matrices of numbers by vectors of bits. The other operations (which are complicated, but only require a modest amount of computation) are performed by the host processor. It is noted that the key point of this work resides in establishing the right design choices. Among these, the most important ones are the rejection of 'neural parallelism', which makes the implementation exact, and the algorithm used to generate random numbers in software, which allows the hardware to be simple. The fact that DECPeRLe-1 makes hardware development cheap and fast was essential in this work.<>
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信