Abstract

This work observes that a large fraction of the computations performed by Deep Neural Networks (DNNs) are intrinsically ineffectual as they involve a multiplication where one of the inputs is zero. This observation motivates Cnvlutin ( CNV ), a value-based approach to hardware acceleration that eliminates most of these ineffectual operations, improving performance and energy over a state-of-the-art accelerator with no accuracy loss. CNV uses hierarchical data-parallel units, allowing groups of lanes to proceed mostly independently enabling them to skip over the ineffectual computations. A co-designed data storage format encodes the computation elimination decisions taking them off the critical path while…

Citation impact

638
total citations
FWCI
46.63
Percentile
100%
References
54
Citations per year

Authors

6

Topics & keywords

Keywords
  • Computer science
  • Multiplication (music)
  • Operand
  • Computation
  • Overhead (engineering)
  • Parallel computing
  • Efficient energy use
  • Fraction (chemistry)
UN Sustainable Development Goals
  • Affordable and clean energy
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