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Location, location, location: the role of spatial locality in asymptotic energy minimization

Published: 11 February 2013 Publication History

Abstract

Locality exploitation is essential to asymptotic energy minimization for gate array netlist evaluation. Naive implementations that ignore locality, including flat crossbars and simple processors based on monolithic memories, can require O(N2) energy for an N node graph. Specifically, it is important to exploit locality (1) to reduce the size of the description of the graph, (2) to reduce data movement, and (3) to reduce instruction movement. FPGAs exploit all three. FPGAs with a Rent Exponent p<0.5 running designs with p<0.5 achieve asymptotically optimal Theta(N) energy. FPGA designs with p>0.5 and implementations with metal layers that grow as O(N(p-0.5)) require only O(N(p+0.5)) energy; this bound can be achieved with O(1) metal layers with a novel multicontext design that has heterogeneous context depth. In contrast, a p>0.5 FPGA design on an implementation technology with O(1) metal layers requires O(N(2p)) energy.

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Appendix for "Location, Location, Location---The Role of Spatial Locality in Asymptotic Energy Minimization'

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cover image ACM Conferences
FPGA '13: Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
February 2013
294 pages
ISBN:9781450318877
DOI:10.1145/2435264
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 February 2013

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Author Tags

  1. FPGA
  2. Rent's rule
  3. VLSI theory
  4. energy
  5. energy complexity
  6. locality
  7. low power
  8. multicontext

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