Book TitleAnalog VLSI Implementation of Neural Systems EditorsCarver Mead, Mohammed Ismail Series TitleThe Springer International Series in Engineering and Computer Science DOIhttps://doi.org/10.1007/978-1-4613-1639-8 PublisherSpringer New York, NY ...
Mead, Analog VLSI and Neural Systems (Addison-Wesley, ... C Koch,HH Li - 《Vision Chips Implementing Vision Algorithms with Analog Vlsi Circuits》 被引量: 173发表: 1994年 Analog VLSI implementation of neural networks The potentialities of CMOS analog VLSI for the implementation of neural ...
Analog VLSI and Neural Systems / Carver A. Mead. Addison-Wesley, Madison, Georgia (1989), 0-201-05992-4doi:10.1016/S0925-2312(89)80020-7V.DavidSánchezA.SDOSNeurocomputing
作者:Carver Mead 出版社:Addison Wesley Publishing Company 出版年:1989-1-1 页数:371 定价:USD 59.99 装帧:Hardcover ISBN:9780201059922 豆瓣评分 目前无人评价 评价: 写笔记 写书评 加入购书单 分享到 推荐 我要写书评 Analog VLSI and Neural Systems的书评 ···(全部 0 条)...
Lyon, and C. Mead. Improved implementation of the silicon cochlea. IEEE Journal Solid-State Circuits, 27(5):692–700, May 1992. CrossRef About this Chapter Title A Low-Power Wide-Dynamic-Range Analog VLSI Cochlea Book Title Neuromorphic Systems Engineering Book Subtitle Neural Networks in ...
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Neuromorphic Computing is the use of very large scale integration (VLSI) systems containing electronic analog circuits to simulate the neuro-biological architectures present in the human brain ad nervous system. Intel Loihi 2, its second-generation neuromorphic research chip. The Akida Neuromorphic Sys...
Rate-0.96 ldpc decoding vlsi for soft-decision error correction of nand flash memory. IEEE Trans. Very Large Scale Integr. Syst. 22, 1004–1015 (2014). Article Google Scholar Mansour, M. M. & Shanbhag, N. R. A 640-Mb/s 2048-bit programmable ldpc decoder chip. IEEE J. Solid-State...
Rogers et al., “An Analog VLSI Implementation of a Multi-Scale Spike Detection Algorithm for Extracellular Neural Recordings” 0-7803-8709-1/05 2005, IEEE, pp. v-viii. Harrison, “A Low-Power Integrated Circuit for Adaptive Detection of Action Potentials in Noisy Signals”Dept. of Electrical...
and digital. The QAFN and its learning algorithm hardware are based on those two approaches simultaneously. Weight reprogrammability is realized based on EEPROM technique that is compatible with CMOS technology. The QAFN and its learning algorithm hardware are suitable to implement in VLSI technology...