A generic systolic array building block for neural networks with on-chip learning
- 1 May 1993
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 4 (3) , 400-407
- https://doi.org/10.1109/72.217181
Abstract
Neural networks require VLSI implementations for on-board systems. Size and real-time considerations show that on-chip learning is necessary for a large range of applications. A flexible digital design is preferred here to more compact analog or optical realizations. As opposed to many current implementations, the two-dimensional systolic array system presented is an attempt to define a novel computer architecture inspired by neurobiology. It is composed of generic building blocks for basic operations rather than predefined neural models. A full custom VLSI design of a first prototype has demonstrated the efficacy of this design. A complete board dedicated to Hopfield's model has been designed using these building blocks. Beyond the very specific application presented, the underlying principles can be used for designing efficient hardware for most neural network models.Keywords
This publication has 14 references indexed in Scilit:
- Digital VLSI multiprocessor design for neurocomputersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- A systolic array exploiting the inherent parallelisms of artificial neural networksMicroprocessing and Microprogramming, 1992
- Systolic architectures for artificial neural netsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- VLSI Design of Neural NetworksPublished by Springer Nature ,1991
- Simulation machine and integrated implementation of neural networksLecture Notes in Computer Science, 1990
- VLSI architectures for neural networksIEEE Micro, 1989
- A VLSI Systolic Array Dedicated to Hopfield Neural NetworkPublished by Springer Nature ,1989
- Automatic synthesis of systolic arrays from uniform recurrent equationsACM SIGARCH Computer Architecture News, 1984
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982
- The perceptron: A probabilistic model for information storage and organization in the brain.Psychological Review, 1958