VLSI for Artificial Intelligence and Neural Networks by Jean-Luc Bechennec, Christophe Chanussot, Vincent Neri,
By Jean-Luc Bechennec, Christophe Chanussot, Vincent Neri, Daniel Etiemble (auth.), José G. Delgado-Frias, William R. Moore (eds.)
This ebook is an edited number of the papers offered on the foreign Workshop on VLSI for Artifidal Intelligence and Neural Networks which was once held on the collage of Oxford in September 1990. Our thank you visit all of the members and particularly to the programme committee for all their exertions. thank you also are as a result of ACM-SIGARCH, the IEEE machine Society, and the lEE for publicizing the development and to the collage of Oxford and SUNY-Binghamton for his or her energetic aid. we're quite thankful to Anna Morris, Maureen Doherty and Laura Duffy for dealing with the executive difficulties. Jose Delgado-Frias Will Moore April 1991 vii PROLOGUE synthetic intelligence and neural community algorithms/computing have elevated in complexity in addition to within the variety of functions. This in tum has posed a massive want for a bigger computational strength than could be supplied through traditional scalar processors that are orientated in the direction of numeric and information manipulations. as a result of the man made intelligence standards (symbolic manipulation, wisdom illustration, non-deterministic computations and dynamic source allocation) and neural community computing process (non-programming and learning), a unique set of constraints and calls for are imposed at the machine architectures for those applications.
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Extra resources for VLSI for Artificial Intelligence and Neural Networks
On') as follows: 0: Reference path to the data object is itself only (single referenced) . • : There may be other reference paths to the data object (multiple referenced). This is shown in Figure 2. 35 Incremental Garbage Collection Scheme in KLl and Its Architectural Support ofPIM REFO>---I"~D LJ REF~ -I Undef REFS Undef REF REF -I REF Figure 2. Definition of MRB The treatment of MRB for pointers to un instantiated variables is a little bit different from this definition. In KLI or other logic programming languages, uninstantiated variables basically are pointed to from two reference paths.
0 X mod P =\= true ! YsO ° = . filter(P, XsO, YsO) . Ys1], filter(P, XsO, Ys1). (1) (2) (3) Figure 1. KL1 program of 'filter' 'prototype') [Shinogi et al 1988], one of the PIM models proposed by ICOT and Fujitsu researchers. Since KLl is a side-effect free language, programming and debugging on parallel machines is expected to be easy. However, a naive implementation of KLl uses a large amount of memory during execution, and invokes garbage collection frequently. General garbage collection on parallel machines is much more difficult and time-consuming than for a single processor.
This architecture (called the SUNY dataflow machine) is a special purpose dataflow computer for the support of artificial intelligence applications. A logic progmmming application is studied in order to show the machine potential. O. R. Moore, Plenum Press, New York. 1991 23 A Dataflow Architecture for AI SUNY DATAFLOW ARCHITECTURE Previous dataflow architectures (Arvind and Culler 1987, Dennis 1987, Dennis 1985, Gurd et aJ 1985, McGraw 1989, Veen 1986, Watson et aJ 1982) as well as AI application requirements (Ciepielewski and Haridi 1984, DeGroot 1984, Hwang and DeGroot 1989, Uhr 1987, Wah 1986) have been considered for the development of the machine.