{"title":"布尔激活的整数卷积神经网络:BoolHash算法","authors":"Grigor Gatchev, V. Mollov","doi":"10.1109/ECCTD49232.2020.9218306","DOIUrl":null,"url":null,"abstract":"Improving the efficiency of convolutional neural networks (CNN) often relies on integer-only algorithms. Using boolean activations can bring further inference speed gain, and can make easier the design of CNN-specific ASICs. A convolutional algorithm called BoolHash that we propose here can additionally increase the inference speed several times, and permits functionalities that usually require more complex processing. A CNN model with 16-bit input weights, 8-bit filter weights and 1-bit activations was used to compare the speed of BoolHash to that of a classic weight-adder convolutional algorithm.","PeriodicalId":336302,"journal":{"name":"2020 European Conference on Circuit Theory and Design (ECCTD)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Integer Convolutional Neural Networks with Boolean Activations: The BoolHash Algorithm\",\"authors\":\"Grigor Gatchev, V. Mollov\",\"doi\":\"10.1109/ECCTD49232.2020.9218306\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Improving the efficiency of convolutional neural networks (CNN) often relies on integer-only algorithms. Using boolean activations can bring further inference speed gain, and can make easier the design of CNN-specific ASICs. A convolutional algorithm called BoolHash that we propose here can additionally increase the inference speed several times, and permits functionalities that usually require more complex processing. A CNN model with 16-bit input weights, 8-bit filter weights and 1-bit activations was used to compare the speed of BoolHash to that of a classic weight-adder convolutional algorithm.\",\"PeriodicalId\":336302,\"journal\":{\"name\":\"2020 European Conference on Circuit Theory and Design (ECCTD)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 European Conference on Circuit Theory and Design (ECCTD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECCTD49232.2020.9218306\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 European Conference on Circuit Theory and Design (ECCTD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECCTD49232.2020.9218306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Integer Convolutional Neural Networks with Boolean Activations: The BoolHash Algorithm
Improving the efficiency of convolutional neural networks (CNN) often relies on integer-only algorithms. Using boolean activations can bring further inference speed gain, and can make easier the design of CNN-specific ASICs. A convolutional algorithm called BoolHash that we propose here can additionally increase the inference speed several times, and permits functionalities that usually require more complex processing. A CNN model with 16-bit input weights, 8-bit filter weights and 1-bit activations was used to compare the speed of BoolHash to that of a classic weight-adder convolutional algorithm.