The aim of this paper is to address the problem of weight storage at the end of the learning phase, an important problem encoun- tered in analog implementation of artificial neural networks. Circuits for weight storage must satisfy essentially two requirements: they must be as simple as possible and they must be able to maintain the informa- tion indefinitely. In this paper we suggest a multilevel (or quantised) weight storage approach which can be easily implemented by standard low complexity circuitry and neither requires refreshing nor conversion.

A Multistable Circuit for Weight Storage in Analog Artificial Neural Networks

CONTI, MASSIMO;ORCIONI, Simone;TURCHETTI, Claudio
1997-01-01

Abstract

The aim of this paper is to address the problem of weight storage at the end of the learning phase, an important problem encoun- tered in analog implementation of artificial neural networks. Circuits for weight storage must satisfy essentially two requirements: they must be as simple as possible and they must be able to maintain the informa- tion indefinitely. In this paper we suggest a multilevel (or quantised) weight storage approach which can be easily implemented by standard low complexity circuitry and neither requires refreshing nor conversion.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/54361
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