| 书目名称 | Hardware Annealing in Analog VLSI Neurocomputing | | 编辑 | Bang W. Lee,Bing J. Sheu | | 视频video | | | 丛书名称 | The Springer International Series in Engineering and Computer Science | | 图书封面 |  | | 描述 | Rapid advances in neural sciences and VLSI design technologies have provided an excellent means to boost the computational capability and efficiency of data and signal processing tasks by several orders of magnitude. With massively parallel processing capabilities, artificial neural networks can be used to solve many engineering and scientific problems. Due to the optimized data communication structure for artificial intelligence applications, a neurocomputer is considered as the most promising sixth-generation computing machine. Typical applica tions of artificial neural networks include associative memory, pattern classification, early vision processing, speech recognition, image data compression, and intelligent robot control. VLSI neural circuits play an important role in exploring and exploiting the rich properties of artificial neural networks by using pro grammable synapses and gain-adjustable neurons. Basic building blocks of the analog VLSI neural networks consist of operational amplifiers as electronic neurons and synthesized resistors as electronic synapses. The synapse weight information can be stored in the dynamically refreshed capacitors for medium-term storage or | | 出版日期 | Book 1991 | | 关键词 | EEPROM; EPROM; Hardware; PROM; Signal; VLSI; analog; circuit; communication; computer; data compression; logic; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-3984-1 | | isbn_softcover | 978-1-4613-6780-2 | | isbn_ebook | 978-1-4615-3984-1Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Springer Science+Business Media New York 1991 |
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发表于 2025-3-21 17:36:00
