Del i serien Synthesis Lectures on Computer Architecture

Space-Time Computing with Temporal Neural Networks

Häftad, Engelska, 2017

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Neuron inputs are encoded as spike patterns, and the neuron is trained to identify input pattern similarities. Similar input patterns are mapped to a much smaller set of similar output patterns, thereby dividing the input patterns into identifiable clusters.

Produktinformation

Utgivningsdatum2017-05-18
Mått191 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieSynthesis Lectures on Computer Architecture
Antal sidor220
FörlagSpringer International Publishing AG
ISBN9783031006265
OriginaltitelSpace-Time Computing with Temporal Neural Networks

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James E. Smith is Professor Emeritus in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. He received his Ph.D. from the University of Illinois in 1976. He then joined the faculty of the University of Wisconsin-Madison, teaching and conducting research-first in fault-tolerant computing, then in computer architecture. In 1979, he took a leave of absence to work for the Control Data Corporation in Arden Hills, MN, participating in the design of the CYBER 180/990. While at Control Data and after returning to the University of Wisconsin in 1981, he studied several aspects of high performance pipelined processors. This work included the development of dynamic history-based branch predictors, instruction issuing methods, and techniques for providing precise interrupts that are widely used today. From 1984-1989, he was principal architect and a logic designer for the ACA ZS-1, a scientific computer employing a dynamically scheduled, superscalar processor architecture. In 1989, Dr. Smith joined Cray Research and headed a small research team that participated in the development and analysis of future supercomputer architectures. This work focused on advanced vector processor implementations, high bandwidth memory systems, and interconnection networks. In 1994, he re-joined the Department of ECE at the University of Wisconsin. His research interests were directed at new paradigms for exploiting instruction level parallelism. The virtual machine abstraction was used as a technique for providing high performance through co-design and tight coupling of hardware and software. In 2007, he retired from Wisconsin, and then conducted research in industry for four years, first at Google, then at Intel. He received the 1999 ACM/IEEE Eckert-Mauchly Award for contributions to computer architecture. Currently, he is studying new neuron-based computing paradigms at home along the Clark Fork near Missoula, Montana.