具有高斯突触的CMOS混合数字模拟可重构神经网络

A.N. Al-Zeftawi, K.M. Abd El-Fattah, H. Shanan, T. Kamel
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引用次数: 1

摘要

在神经形态领域,不同的工程问题需要不同的神经网络拓扑结构。基于这个概念,我们的动机是构建一个可重构的神经网络芯片。分布式高斯神经元突触是一种新的突触类型。此外,还增加了一种新的改进分辨率的电流模式赢家通吃电路,以实现自组织拓扑结构。芯片被组织成4个部分连接的块,每个块有4/spl倍/3个完全连接的神经元。该芯片采用1.2 /spl mu/m AMI CMOS工艺,面积为2mm /spl × / 2mm,采用MOSIS工艺制备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CMOS mixed digital analog reconfigurable neural network with Gaussian synapses
In the neuromorphic arena, different engineering problems require different neural network topologies. In view of this concept, the motivation was to build a reconfigurable neural network chip. The distributed Gaussian-neuron synapse is introduced as a new type of synapses. Also a new improved resolution current-mode winner-takes-all circuit is added to realize a self-organizing topology. The chip is organized into 4 partially connected tiles with 4/spl times/3 fully connected neurons per tile. The chip was fabricated through MOSIS in 1.2 /spl mu/m AMI CMOS process occupying an area of 2 mm /spl times/2 mm.
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