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http://dx.doi.org/10.14372/IEMEK.2020.15.5.235

Analysis of Reduced-Width Truncated Mitchell Multiplication for Inferences Using CNNs  

Kim, HyunJin (Dankook University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.15, no.5, 2020 , pp. 235-242 More about this Journal
Abstract
This paper analyzes the effect of reduced output width of the truncated logarithmic multiplication and application to inferences using convolutional neural networks (CNNs). For small hardware overhead, output width is reduced in the truncated Mitchell multiplier, so that fractional bits in multiplication output are minimized in error-resilient applications. This analysis shows that when reducing output width in the truncated Mitchell multiplier, even though worst-case relative error increases, average relative error can be kept small. When adopting 8 fractional bits in multiplication output in the evaluations, there is no significant performance degradation in target CNNs compared to existing exact and original Mitchell multipliers.
Keywords
Approximate computing; Convolutional neural network; Logarithmic multiplication; Mitchell algorithm; Reduced-width multiplier;
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