title: Weight perturbation: an optimal architecture and learning technique for analog VLSI feedforward and recurrent multilayer networks.
abstract: 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.
authors.affiliation: Sch. of Electr. Eng., Sydney Univ., NSW.
authors.forename: M
authors.initials: M
authors.lastname: Jabri
authors.forename: B
authors.initials: B
authors.lastname: Flower