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A Learning Algorithm for a Neural Network LSI with Restricted Integer Weights
Tomohisa KIMURA Takeshi SHIMA
IEICE TRANSACTIONS on Electronics
Publication Date: 1997/07/25
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Issue on New Concept Device and Novel Architecture LSIs)
Category: Neural Networks and Chips
neural networks, learning, analog, LSI, accuracy,
Full Text: PDF(536.4KB)>>
A novel learning algorithm for a neural network LSI which has low resolution synapse weights is proposed. Following a brief discussion of the synapse weight adaptation mechanism in the gradient descent scheme, we propose a way of achieving relaxation from the influence of discretized weight. Restriction of the number of synapses to be updated in one learning iteration is effective to relax the influence. Simulation results support the effectiveness of this learning algorithm. Low resolution synapses will be practical to realize large-scale neural network LSIs.