Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization

被引:89
作者
Cassidy, Andrew S. [1 ]
Georgiou, Julius [2 ]
Andreou, Andreas G. [1 ,2 ]
机构
[1] Johns Hopkins Univ, Dept Elect & Comp Engn, Baltimore, MD 21218 USA
[2] Univ Cyprus, Dept Elect & Comp Engn, CY-1678 Nicosia, Cyprus
基金
美国国家科学基金会;
关键词
Silicon brains; Neuromorphic engineering; Silicon neurons; Learning in silicon; FPGA neural arrays; MULTICHIP NEUROMORPHIC SYSTEM; IMAGE SENSOR; PROCESSOR; ANALOG; VISION; ARRAY; NETWORKS; MODEL; PLASTICITY; COMMUNICATION;
D O I
10.1016/j.neunet.2013.05.011
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present a design framework for neuromorphic architectures in the nano-CMOS era. Our approach to the design of spiking neurons and STDP learning circuits relies on parallel computational structures where neurons are abstracted as digital arithmetic logic units and communication processors. Using this approach, we have developed arrays of silicon neurons that scale to millions of neurons in a single state-of-the-art Field Programmable Gate Array (FPGA). We demonstrate the validity of the design methodology through the implementation of cortical development in a circuit of spiking neurons, STDP synapses, and neural architecture optimization. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:4 / 26
页数:23
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