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A Case Study on Hardware/Software Codesign in Embedded Artificial Neural Networks

  • Jonathan Parri
  • John-Marc Desmarais
  • Daniel Shapiro
  • Miodrag Bolic
  • Voicu Groza
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 1)

Abstract

Software/hardware codesign is a complex research problem that has been slowly making headway into industry-ready system design products. Recent advances have shown viability to this direction within the design space exploration scope, especially with regards to rapid development cycles. Here, we exploit the hardware/software codesign landscape in the artificial neural network problem space. Automated tools requiring minimal technical expertise from Altera and Tensilica are examined along with newer advances solely within hardware/software codesign research domain. The design space exploration options discussed here look to achieve better software/hardware partitions using instruction-set extensions and coprocessors. As neural networks continue to find usage in embedded systems, it has become imperative to efficiently optimize their implementation within a short development cycle. Modest speedups can be easily achieved with these automated hardware/software codesign tools on the benchmarks examined.

Keywords

Clock Cycle Design Space Exploration Bidirectional Associative Memory Custom Instruction Hardware Accelerator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Jonathan Parri
    • 1
  • John-Marc Desmarais
    • 1
  • Daniel Shapiro
    • 1
  • Miodrag Bolic
    • 1
  • Voicu Groza
    • 1
  1. 1.Computer Architecture Research Group, School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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