PLAM: a Posit Logarithm-Approximate Multiplier for Power Efficient Posit-based DNNs

• URL: http://arxiv.org/abs/2102.09262v1
• Date: Thu, 18 Feb 2021 10:43:07 GMT
• Title: PLAM: a Posit Logarithm-Approximate Multiplier for Power Efficient Posit-based DNNs
• Authors: Raul Murillo, Alberto A. Del Barrio, Guillermo Botella, Min Soo Kim, HyunJin Kim and Nader Bagherzadeh
• Abstract summary: The Posit Number System was introduced in 2017 as a replacement for floating-point numbers. This paper proposes a Posit Logarithm-Approximate multiplication scheme to significantly reduce the complexity of 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.
• Score: 8.623938357911467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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