{"title":"基于模拟忆阻器的图像识别神经形态交叉电路","authors":"Lingfeng Xu, Chuandong Li, Ling Chen","doi":"10.1109/ICICIP.2015.7388161","DOIUrl":null,"url":null,"abstract":"Since its discovery, memristor has been well studied by researchers from all around the world, and its application in recognition proves to be very promising. In this paper, we modify a memristor crossbar circuit from an existing work to recognize 8 × 8 pixel binary images. We use analog memristors instead of binary memristors to complete the circuit. The simulated recognition rate is 82.5% in average, and we step further by carrying out a Monte Carlo simulation to analyze the performances of the circuit under different memristance variations and statistical distributions. We find that as the memristance variation rises up, the recognition rate under Gaussian distribution drops quickly, while the performance under uniform distribution is relatively stable. In the final part, we provide some outlooks and remarks on the possible improvements of the circuit.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Analog memristor based neuromorphic crossbar circuit for image recognition\",\"authors\":\"Lingfeng Xu, Chuandong Li, Ling Chen\",\"doi\":\"10.1109/ICICIP.2015.7388161\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since its discovery, memristor has been well studied by researchers from all around the world, and its application in recognition proves to be very promising. In this paper, we modify a memristor crossbar circuit from an existing work to recognize 8 × 8 pixel binary images. We use analog memristors instead of binary memristors to complete the circuit. The simulated recognition rate is 82.5% in average, and we step further by carrying out a Monte Carlo simulation to analyze the performances of the circuit under different memristance variations and statistical distributions. We find that as the memristance variation rises up, the recognition rate under Gaussian distribution drops quickly, while the performance under uniform distribution is relatively stable. In the final part, we provide some outlooks and remarks on the possible improvements of the circuit.\",\"PeriodicalId\":265426,\"journal\":{\"name\":\"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)\",\"volume\":\"2017 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICIP.2015.7388161\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIP.2015.7388161","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analog memristor based neuromorphic crossbar circuit for image recognition
Since its discovery, memristor has been well studied by researchers from all around the world, and its application in recognition proves to be very promising. In this paper, we modify a memristor crossbar circuit from an existing work to recognize 8 × 8 pixel binary images. We use analog memristors instead of binary memristors to complete the circuit. The simulated recognition rate is 82.5% in average, and we step further by carrying out a Monte Carlo simulation to analyze the performances of the circuit under different memristance variations and statistical distributions. We find that as the memristance variation rises up, the recognition rate under Gaussian distribution drops quickly, while the performance under uniform distribution is relatively stable. In the final part, we provide some outlooks and remarks on the possible improvements of the circuit.