Asynchronous VLSI neural networks using pulse-stream arithmetic
Asynchronous VLSI neural networks using pulse-stream arithmetic
复制标题
使用脉冲流算法的异步 VLSI 神经网络
DOI：
10.1109/4.307
复制
发表时间：
1988
影响因子：
5.4
通讯作者：
Anthony V. W. Smith
中科院分区：
文献类型：
--
作者：
A. Murray;Anthony V. W. Smith
探讨了神经网络与超大规模集成电路之间的关系。神经网络的介绍将Hopfield模型和Delta学习规则与S. Grossberg（1968）对神经动力学的描述。描述了一种模拟生物神经网络的计算风格，使用脉冲流信号和模拟求和。数字可编程权重允许构建学习网络。神经和突触功能的功能和结构形式，沿着与模拟结果。最后，在3 μ m CMOS实现的神经网络的初步测量。>
The relationship between neural networks and VLSI is explored. An introduction to neural networks relates the Hopfield model and the Delta learning rule to S. Grossberg's (1968) description of neural dynamics. A computational style that mimics that of a biological neural network, using pulse-stream signaling and analog summation, is described. Digitally programmable weights allow learning networks to be constructed. Functional and structural forms of neural and synaptic functions are presented, along with simulation results. Finally a neural network implemented in 3- mu m CMOS is presented with preliminary measurements. >