A purely capacitive synaptic matrix for fixed-weight neural networks
A purely capacitive synaptic matrix for fixed-weight neural networks
复制标题
用于固定权重神经网络的纯电容突触矩阵
DOI：
10.1109/31.68299
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发表时间：
1991
期刊：
影响因子：
--
通讯作者：
U. Çilingiroğlu
中科院分区：
文献类型：
--
作者：
U. Çilingiroğlu
结果表明，固定权重神经网络的突触功能可以仅使用一个电容器来实现。由此产生的突触矩阵没有有源器件，可提供非常高的空间功率效率和速度，以及具有相当大的模拟深度的大突触容量。在树突电荷守恒的基础上分析了通用电容器矩阵。结果用于确定网络限制并设计双多晶 CMOS 前馈分类器，该分类器能够纠正在一组 30 个 16-b 代码模式中出现的任何 3-b 错误。对于该特定设计中采用的非常保守的3μm规则，每个突触占据16.5μm*10μm的场氧化物空间。通过仿真验证电气性能。还包括所提出的网络与其他开关电容器神经网络配置之间的比较。 >
It is shown that the synaptic function of fixed-weight neural networks can be implemented using only one capacitor. The resulting synaptic matrix, being devoid of active devices, offers very high space-power efficiency and speed along with large synapse capacity with considerable analog depth. The generic capacitor matrix is analyzed on the basis of dendritic charge conservation. The results are used to determine network limitations and to design a double-poly CMOS feedforward classifier that is capable of correcting any 3-b error occurring in a set of thirty 16-b code-patterns. Each synapse occupies 16.5 mu m*10 mu m of field-oxide space for the very conservative 3- mu m rules employed in this particular design. Electrical performance is verified through simulation. Comparison between the proposed network and other switched-capacitor neural network configurations is also included. >