Dynamically Adaptable CMOS Winner-Take-All Neural Network

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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Kunihiko Iizuka, Masayuki Miyamoto, Hirofumi Matsui


The major problem that has prevented practical application of analog neuro-LSIs has been poor accuracy due to fluctuating analog device characteristics inherent in each device as a result of manufacturing. This paper proposes a dynamic control architecture that allows analog silicon neural networks to compensate for the fluctuating device characteristics and adapt to a change in input DC level. We have applied this architecture to compensate for input offset voltages of an analog CMOS WTA (Winner-Take-AlI) chip that we have fabricated. Experimental data show the effectiveness of the architecture.