Stochastic arithmetic implementations of neural networks with in situ learning
J.A. Dickson, Robert D. McLeod, HOWARD C. CARD · 2002
The implementation of artificial neural networks using stochastic arithmetic capable of in situ learning is described. Stochastic arithmetic uses values encoded as a pulse density, and allows addition, multiplication, and the nonlinearity to be implemented in a very small amount of digital hardware. A VLSI implementation of such a network is capable of processing 100000 training vectors per second. The performance of this architecture is demonstrated by two examples.>