AN ALL-ANALOG EXPANDABLE NEURAL-NETWORK LSI WITH ON-CHIP BACKPROPAGATION LEARNING

This paper proposes an all-analog neural network LSI architecture and a new learning procedure called contrastive backpropagation learning. In analog neural LSI's with on-chip backpropagation learning, inevitable offset errors that arise in the learning circuits seriously degrade the learning performance. Using the learning procedure proposed here, offset errors are canceled to a large extent and the effect of offset errors on the learning performance is minimized. This paper also describes a prototype LSI with 9 neurons and 81 synapses based on the proposed architecture which is capable of continuous neuron-state and continuous-time operation because of its fully analog and fully parallel property. Therefore, an analog neural system made by combining LSI's with feedback connections is promising for implementing continuous-time models of recurrent networks with real-time learning.