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…
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638
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- FWCI
- 46.63
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Authors
6Topics & keywords
Topics
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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