PLAM: A Posit Logarithm-Approximate Multiplier
Raúl Murillo, Alberto A. Del Barrio, Guillermo Botella, Min Soo Kim, HyunJin Kim, Nader Bagherzadeh
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 several areas, such as deep learning, and produced some unit designs which are still far from being competitive with their floating-point counterparts. This article proposes a Posit Logarithm-Approximate Multiplication (PLAM) scheme to significantly reduce the complexity of posit multipliers, one of the most power-hungry arithmetic units. The impact of this approach is evaluated in deep neural network inference, where there are no significant accuracy drops. Compared with state-of-the-art posit multipliers, experiments show that the proposed technique reduces the area, power, and delay of 32-bit hardware multipliers up to 72.86%, 81.79%, and 17.01%, respectively.