Abstract
This paper discusses the current state of the art of industrial neurocomputing, and then speculates on its future.
Three examples of commercial neuro-silicon are presented: the Adaptive Solutions CNAPS system, the Intel ETANN chip, and the Synaptics OCR chip.
We then speculate on where commercial neurocomputing hardware is going. In particular we propose that commercial systems will evolve in the direction of capturing more contextual, knowledge level information. Some results of an industrial handwritten character recognition system created at Apple Computers will be presented which demonstrate the power of adding contextual knowledge to neural network based recognition. Also discussed will be some of the possible directions required for neural network algorithms needed to capture such knowledge and utilize it effectively, as well as results from experiments on capturing contextual knowledge using several different neural network algorithms.
Finally, the issues involved in designing VLSI architectures for the efficient emulation of sparsely activated, sparsely connected contextual networks will be discussed. There are fundamental cost/performance limits when emulating such sparse structures in both the digital and analog domain.
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Hammerstrom, D., Rehfuss, S. Neurocomputing hardware: present and future. Artif Intell Rev 7, 285–300 (1993). https://doi.org/10.1007/BF00849056
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DOI: https://doi.org/10.1007/BF00849056