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 μm 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.

High-speed VLSI fuzzy processors designed for HEPE

Russo Marco
1996

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 μm 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.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/481552
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact