一种用于人工神经网络计算的变精度收缩结构

Proceedings of Fifth International Conference on Microelectronics for Neural Networks Pub Date : 1996-02-12 DOI:10.1109/MNNFS.1996.493814

Amine Bermak, D. Martinez

{"title":"一种用于人工神经网络计算的变精度收缩结构","authors":"Amine Bermak, D. Martinez","doi":"10.1109/MNNFS.1996.493814","DOIUrl":null,"url":null,"abstract":"When Artificial Neural Networks (ANNs) are implemented in VLSI with fixed precision arithmetic, the accumulation of numerical errors may lead to results which are completely inaccurate. To avoid this, we propose a variable-precision arithmetic in which the precision of the computation is specified by the user at each layer in the network. This paper presents a top-down approach for designing an efficient bit-level systolic architecture for variable precision neural computation.","PeriodicalId":151891,"journal":{"name":"Proceedings of Fifth International Conference on Microelectronics for Neural Networks","volume":"134 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A variable-precision systolic architecture for ANN computation\",\"authors\":\"Amine Bermak, D. Martinez\",\"doi\":\"10.1109/MNNFS.1996.493814\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When Artificial Neural Networks (ANNs) are implemented in VLSI with fixed precision arithmetic, the accumulation of numerical errors may lead to results which are completely inaccurate. To avoid this, we propose a variable-precision arithmetic in which the precision of the computation is specified by the user at each layer in the network. This paper presents a top-down approach for designing an efficient bit-level systolic architecture for variable precision neural computation.\",\"PeriodicalId\":151891,\"journal\":{\"name\":\"Proceedings of Fifth International Conference on Microelectronics for Neural Networks\",\"volume\":\"134 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Fifth International Conference on Microelectronics for Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MNNFS.1996.493814\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Fifth International Conference on Microelectronics for Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MNNFS.1996.493814","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

在固定精度算法的超大规模集成电路中实现人工神经网络时，数值误差的累积可能导致结果完全不准确。为了避免这种情况，我们提出了一种变精度算法，其中计算的精度由用户在网络中的每一层指定。本文提出了一种自顶向下的方法来设计一种有效的位级收缩结构，用于变精度神经计算。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A variable-precision systolic architecture for ANN computation

When Artificial Neural Networks (ANNs) are implemented in VLSI with fixed precision arithmetic, the accumulation of numerical errors may lead to results which are completely inaccurate. To avoid this, we propose a variable-precision arithmetic in which the precision of the computation is specified by the user at each layer in the network. This paper presents a top-down approach for designing an efficient bit-level systolic architecture for variable precision neural computation.

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

Proceedings of Fifth International Conference on Microelectronics for Neural Networks

自引率

0.00%

发文量