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.
@article{arxiv.2102.09262,
title = {PLAM: a Posit Logarithm-Approximate Multiplier},
author = {Raul Murillo and Alberto A. Del Barrio and Guillermo Botella and Min Soo Kim and HyunJin Kim and Nader Bagherzadeh},
journal= {arXiv preprint arXiv:2102.09262},
year = {2021}
}