An Analog CMOS Approximate Identity Neural Network with Stochastic Learning and Multilevel Weight Storage

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概要

• 論文の詳細を見る

• In this paper CMOS VLSI circuit solutions are suggested for on-chip learning and weight storage, which are simple and silicon area efficient. In particular a stochastic learning scheme, named Random Weight Change, and a multistable weight storage approach have been implemented. Additionally, the problems of the influence of technological variations on learning accuracy is discussed. Even though both the learning scheme and the weight storage are quite general, in the paper we will refer to a class of networks, named Approximate Identity Neural Networks, which are particularly suitable to be implemented with analog CMOS circuits. As a test vehicle a small network with four neurons, 16 weights, on chip learning and weight storage has been fabricated in a 1.2μm double-metal CMOS process.

• 社団法人電子情報通信学会の論文
• 1999-07-25

著者

• Orcioni S

  Univ. Ancona Ancona Ita

• Conti M

  Univ. Ancona Ancona Ita

• Orcioni Simone

  Dipartimento Di Elettronica Ed Automatica University Of Ancona

• CONTI Massimo

  Dipartimento di Elettronica ed Automatica, University of Ancona

• CRIPPA Paolo

  Dipartimento di Elettronica ed Automatica, University of Ancona

• GUAITINI Giovanni

  Innovative Systems Design Group - STMicroelectronics

• TURCHETTI Claudio

  Dipartimento di Elettronica ed Automatica, University of Ancona

• Conti M

  Dipartimento Di Elettronica Ed Automatica University Of Ancona

• Crippa Paolo

  Dipartimento Di Elettronica Ed Automatica University Of Ancona

• Turchetti Claudio

  Dipartimento Di Elettronica Ed Automatica University Of Ancona

関連論文

• An Analog CMOS Approximate Identity Neural Network with Stochastic Learning and Multilevel Weight Storage
• A Class of Neural Networks Based on Approximate Identity for Analog IC's Hardware Implementation
• Artificial neural networks as approximators of stochastic processes

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