Weight perturbation: an optimal architecture and learning technique for analog VLSI feedforward and recurrent multilayer networks.
Previous work on analog VLSI implementation of multilayer perceptrons with on-chip learning has mainly targeted the implementation of algorithms such as back-propagation. Although back-propagation is efficient, its implementation in analog VLSI requires excessive computational hardware. It is shown that using gradient descent with direct approximation of the gradient instead of back-propagation is more economical for parallel analog implementations. It is shown that this technique (which is called ;weight perturbation') is suitable for multilayer recurrent networks as well. A discrete level analog implementation showing the training of an XOR network as an example is presented.
Jabri
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Sch. of Electr. Eng., Sydney Univ., NSW.
Flower
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