Share Email Print

Proceedings Paper

High-speed VLSI fuzzy processors designed for HEPE
Author(s): Alessandro Gabrielli; Enzo Gandolfi; Massimo Masetti; Marco Russo
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Neural chips now are used in the trigger devices for HEPE. Three years ago we talked the problem of using also fuzzy chip microprocessors because a fuzzy system in principle can work as a neural system and is more flexible. We made them a comparison between the two approaches and the conclusions were: fuzzy chips running at a speed suitable for trigger devices were not available on the market, therefore one should have to design his own VLSI chip while, for the neural solution, one can use commercial chips or design a dedicated VLSI chip; the fuzzy solution requires an expert to develop the fuzzy system, that is the rules, while the neural solution requires a training phase; the fuzzy solution is more flexible because you known its knowledge basis and you can improve on-line the related performances by changing the rules. To day this situation is improved because there are SW tools, called Rule Generators, able to develop a fuzzy system by means of Neural Network or Genetic Algorithms. This paper starts with a comparison between Neural Networks and Fuzzy Logic with the aim to summarize the advantages of using both the HEPE trigger devices, then are described the chips already constructed or designed: a first 1 micrometers VLSI fuzzy chip with four 7 bits input and one output running at 50 Mega Fuzzy Inference per Second therefore its processing rate depends upon the fuzzy system to process; a second one, which will be sent to the foundry next march with four 7 bit inputs running at a rate of 300 ns whichever is the fuzzy system; a third one, now in design phase, with 8 - 16 inputs running at 100 - 50 MFIPS with a rule selector to further reduce the total processing speed.

Paper Details

Date Published: 22 March 1996
PDF: 12 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235975
Show Author Affiliations
Alessandro Gabrielli, Univ. of Bologna (Italy)
Enzo Gandolfi, Univ. of Bologna (Italy)
Massimo Masetti, Univ. of Bologna (Italy)
Marco Russo, Univ. of Catania (Italy)

Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

© SPIE. Terms of Use
Back to Top