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arXiv · 2102.09262

PLAM: a Posit Logarithm-Approximate Multiplier

Raul Murillo·Alberto A. Del Barrio·Guillermo Botella·Min Soo Kim·HyunJin Kim·Nader Bagherzadeh
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Abstract

The Posit Number System was introduced in 2017 as a replacement for floating-point numbers. Since then, the community has explored its application in Neural Network related tasks and produced some unit designs which are still far from being competitive with their floating-point counterparts. This paper proposes a Posit Logarithm-Approximate Multiplication (PLAM) scheme to significantly reduce the complexity of posit multipliers, the most power-hungry units within Deep Neural Network architectures. When comparing with state-of-the-art posit multipliers, experiments show that the proposed technique reduces the area, power, and delay of hardware multipliers up to 72.86%, 81.79%, and 17.01%, respectively, without accuracy degradation.

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Raul Murillo, Alberto A. Del Barrio, Guillermo Botella, Min Soo Kim, HyunJin Kim, Nader Bagherzadeh. 2021-02-18. PLAM: a Posit Logarithm-Approximate Multiplier. https://doi.org/10.1109/tetc.2021.3109127

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