Single chip VLSI realization of a neural net for fast decision making functions

F. Stupmann, S. Rode, G. Geske
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Abstract

The newest results of a hardware realization of a neural net for fast decision making functions in real time are shown here. There is a digital micro core with any functions-proceeding of the learning and testing of the net, supervising of training process and computation of some calculations in pre- and post-processing. The decision-making function is a trainable integrated analog neural network structure. The circuit not only contains the reproduction path but also the learning on-chip. Learning patterns for the neural chip are provided in a memory unit. These patterns are automatically presented to the network. The process of weight change (i.e. learning) is fully integrated. The information processing speed from the input to the output of the chip is 2 /spl mu/s in the reproduction process. The number of neurons integrated in the whole chip is 100 in the input layer, 60 in the hidden layer and 10 in the output layer. The back propagation algorithm is implemented in an analog circuit.
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单片机VLSI实现了一个神经网络快速决策的功能
这里展示了用于实时快速决策功能的神经网络硬件实现的最新结果。有一个数字微核,可以进行网络的学习和测试,训练过程的监控,以及预处理和后处理中一些计算的计算。决策函数是一个可训练的集成模拟神经网络结构。该电路不仅包含复制路径,还包含片上学习。神经芯片的学习模式在存储单元中提供。这些模式自动呈现给网络。权重变化(即学习)的过程是完全整合的。在再现过程中,芯片从输入到输出的信息处理速度为2 /spl mu/s。整个芯片集成的神经元数量为输入层100个,隐藏层60个,输出层10个。该反向传播算法在模拟电路中实现。
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