{"title":"用于人工神经网络的芯片和计算机","authors":"M. Bogdan, H. Speckmann, W. Rosenstiel","doi":"10.1002/1616-8984(199610)2:1<105::AID-SEUP105>3.0.CO;2-Q","DOIUrl":null,"url":null,"abstract":"In this chapter we have described a small number of chips and neurocomputers for artificial neural nets. The architectures presented are commercially available. As examples, we have presented two chips (ETANN and Nestor / Intel Ni100), two neurocomputers based on standard components (MUSIC, HNC SNAP), and two neurocomputers (CNAPS, SYNAPSE) with special chips (X1 and MA16, respectively). An overview is given in Table 6-1. The following abbreviations are used: NP for number of processors, M for memory, WL for word length, SISD for single instruction single data, SIMD for single instruction multiple data, MIMD for multiple instructions multiple data, BP for back-propagation and SOM for Kohonens self-organizing map. The presented values are taken from [3, 5, 7] and [13].","PeriodicalId":154848,"journal":{"name":"Sensors Update","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Chips and Computers for Artificial Neural Nets\",\"authors\":\"M. Bogdan, H. Speckmann, W. Rosenstiel\",\"doi\":\"10.1002/1616-8984(199610)2:1<105::AID-SEUP105>3.0.CO;2-Q\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this chapter we have described a small number of chips and neurocomputers for artificial neural nets. The architectures presented are commercially available. As examples, we have presented two chips (ETANN and Nestor / Intel Ni100), two neurocomputers based on standard components (MUSIC, HNC SNAP), and two neurocomputers (CNAPS, SYNAPSE) with special chips (X1 and MA16, respectively). An overview is given in Table 6-1. The following abbreviations are used: NP for number of processors, M for memory, WL for word length, SISD for single instruction single data, SIMD for single instruction multiple data, MIMD for multiple instructions multiple data, BP for back-propagation and SOM for Kohonens self-organizing map. The presented values are taken from [3, 5, 7] and [13].\",\"PeriodicalId\":154848,\"journal\":{\"name\":\"Sensors Update\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sensors Update\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/1616-8984(199610)2:1<105::AID-SEUP105>3.0.CO;2-Q\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sensors Update","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/1616-8984(199610)2:1<105::AID-SEUP105>3.0.CO;2-Q","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this chapter we have described a small number of chips and neurocomputers for artificial neural nets. The architectures presented are commercially available. As examples, we have presented two chips (ETANN and Nestor / Intel Ni100), two neurocomputers based on standard components (MUSIC, HNC SNAP), and two neurocomputers (CNAPS, SYNAPSE) with special chips (X1 and MA16, respectively). An overview is given in Table 6-1. The following abbreviations are used: NP for number of processors, M for memory, WL for word length, SISD for single instruction single data, SIMD for single instruction multiple data, MIMD for multiple instructions multiple data, BP for back-propagation and SOM for Kohonens self-organizing map. The presented values are taken from [3, 5, 7] and [13].