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Title: Neural algorithms on VLSI concurrent architectures

Conference · · Neural Networks; (United States)
OSTI ID:6242818

The research concerns the study of neural algorithms for developing CAD tools with A.I. features in VLSI design activities. In this paper the focus is on optimization problems such as partitioning, placement and routing. These problems require massive computational power to be solved (NP-complete problems) and the standard approach is usually based on euristic techniques. Neural algorithms can be represented by a circuital model. This kind of representation can be easily mapped in a real circuit, which, however, features limited flexibility with respect to the variety of problems. In this sense the simulation of the neural circuit, by mapping it on a digital VLSI concurrent architecture seems to be preferrable; in addition this solution offers a wider choice with regard to algorithms characteristics (e.g. transfer curve of neural elements, reconfigurability of interconnections, etc.). The implementation with programmable components, such as transputers, allows an indirect mapping of the algorithm (one transputer for N neurons) accordingly to the dimension and the characteristics of the problem. In this way the neural algorithm described by the circuit is reduced to the algorithm that simulates the network behavior. The convergence properties of that formulation are studied with respect to the characteristics of the neural element transfer curve.

Research Organization:
Universita' di Genova, Via all'Opera Pia 11a-I-16145, Genova, Italy (IT)
OSTI ID:
6242818
Report Number(s):
CONF-8809132-
Journal Information:
Neural Networks; (United States), Vol. 1:1; Conference: 1. International Neural Network Society annual meeting, Boston, MA, USA, 6 Sep 1988
Country of Publication:
United States
Language:
English