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Title: Scaling to Nanotechnology Limits with the PIMS Computer Architecture and a new Scaling Rule

Abstract

We describe a new approach to computing that moves towards the limits of nanotechnology using a newly formulated sc aling rule. This is in contrast to the current computer industry scali ng away from von Neumann's original computer at the rate of Moore's Law. We extend Moore's Law to 3D, which l eads generally to architectures that integrate logic and memory. To keep pow er dissipation cons tant through a 2D surface of the 3D structure requires using adiabatic principles. We call our newly proposed architecture Processor In Memory and Storage (PIMS). We propose a new computational model that integrates processing and memory into "tiles" that comprise logic, memory/storage, and communications functions. Since the programming model will be relatively stable as a system scales, programs repr esented by tiles could be executed in a PIMS system built with today's technology or could become the "schematic diagram" for implementation in an ultimate 3D nanotechnology of the future. We build a systems software approach that offers advantages over and above the technological and arch itectural advantages. Firs t, the algorithms may be more efficient in the conventional sens e of having fewer steps. Second, the algorithms may run with higher power efficiencymore » per operation by being a better match for the adiabatic scaling ru le. The performance analysis based on demonstrated ideas in physical science suggests 80,000 x improvement in cost per operation for the (arguably) gene ral purpose function of emulating neurons in Deep Learning.« less

Authors:
 [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1171939
Report Number(s):
SAND2015-1318
567181
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Debenedictis, Erik P. Scaling to Nanotechnology Limits with the PIMS Computer Architecture and a new Scaling Rule. United States: N. p., 2015. Web. doi:10.2172/1171939.
Debenedictis, Erik P. Scaling to Nanotechnology Limits with the PIMS Computer Architecture and a new Scaling Rule. United States. doi:10.2172/1171939.
Debenedictis, Erik P. Sun . "Scaling to Nanotechnology Limits with the PIMS Computer Architecture and a new Scaling Rule". United States. doi:10.2172/1171939. https://www.osti.gov/servlets/purl/1171939.
@article{osti_1171939,
title = {Scaling to Nanotechnology Limits with the PIMS Computer Architecture and a new Scaling Rule},
author = {Debenedictis, Erik P.},
abstractNote = {We describe a new approach to computing that moves towards the limits of nanotechnology using a newly formulated sc aling rule. This is in contrast to the current computer industry scali ng away from von Neumann's original computer at the rate of Moore's Law. We extend Moore's Law to 3D, which l eads generally to architectures that integrate logic and memory. To keep pow er dissipation cons tant through a 2D surface of the 3D structure requires using adiabatic principles. We call our newly proposed architecture Processor In Memory and Storage (PIMS). We propose a new computational model that integrates processing and memory into "tiles" that comprise logic, memory/storage, and communications functions. Since the programming model will be relatively stable as a system scales, programs repr esented by tiles could be executed in a PIMS system built with today's technology or could become the "schematic diagram" for implementation in an ultimate 3D nanotechnology of the future. We build a systems software approach that offers advantages over and above the technological and arch itectural advantages. Firs t, the algorithms may be more efficient in the conventional sens e of having fewer steps. Second, the algorithms may run with higher power efficiency per operation by being a better match for the adiabatic scaling ru le. The performance analysis based on demonstrated ideas in physical science suggests 80,000 x improvement in cost per operation for the (arguably) gene ral purpose function of emulating neurons in Deep Learning.},
doi = {10.2172/1171939},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Feb 01 00:00:00 EST 2015},
month = {Sun Feb 01 00:00:00 EST 2015}
}

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