Proceedings ArticleDOI
Digital Systems for Neural Networks
Paolo Ienne,Gary M. Kuhn +1 more
- Vol. 10279
TLDR
In this paper, the authors present an overview of digital systems to implement neural networks, including serial computers, parallel systems with standard digital components, and parallel system with special-purpose digital devices, with an emphasis on commercially available systems.Abstract:
Neural networks are non-linear static or dynamical systems that learn to solve problems from examples Those learning algorithms that require a lot of computing power could benefit from fast dedicated hardware This paper presents an overview of digital systems to implement neural networks We consider three options for implementing neural networks in digital systems: serial computers, parallel systems with standard digital components, and parallel systems with special-purpose digital devices We describe many examples under each option, with an emphasis on commercially available systems We discuss the trend toward more general architectures, we mention a few hybrid and analog systems that can complement digital systems, and we try to answer questions that came to our minds as prospective users of these systems We conclude that support software and in general, system integration, is beginning to reach the level of versatility that many researchers will require The next step appears to be integrating all of these technologies together, in a new generation of big, fast and user-friendly neurocomputersread more
Citations
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Deterministic bit-stream digital neurons
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Design, Implementation, and Test of a Multi-Model Systolic Neural-Network Accelerator
TL;DR: Non-Linear Modelling and Neural Network Reference LANOS-CONF-1994-018 is presented, which describes the design of a convolutional neural network based on the explicit specification of a TSP.