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On embeddings of neural networks into massively parallel computer systems

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1 Author(s)
Bin Cong ; Dept. of Comput. Sci., South Dakota State Univ., Brookings, SD, USA

Artificial neural networks (ANNs) have many characteristics that are suitable for massively parallel computation: simple processing units (neurons), small local memory requirement for each neuron, highly parallel operations. Naturally, neural network implementation should be a target for massively parallel computing. This paper presents and discusses techniques to map a neural network algorithm onto a massively parallel computer system. The goal is to maximize parallelism by breaking the ANN computation into basic units and processing these units in parallel. The following strategies are discussed: (1) design special highly parallel computers for artificial neural networks; (2) map the neural network algorithms directly onto the existing general-purpose parallel computers; (3) map the neural network algorithms onto the optical bus based systems; (4) design new structured neural networks that are similar to the topologies of the existing parallel systems; (5) use the divide-and-conquer technique to break a large neural network into many small ones, each will be processed by a PC or workstation

Published in:

Aerospace and Electronics Conference, 1997. NAECON 1997., Proceedings of the IEEE 1997 National  (Volume:1 )

Date of Conference:

14-18 Jul 1997