Murillo Montero, RaúlDel Barrio García, Alberto AntonioBotella Juan, GuillermoKim, Min SooKim, HyunJinBagherzadeh, Nader2026-02-192026-02-192021-09-06R. Murillo, A. A. Del Barrio, G. Botella, M. S. Kim, H. Kim and N. Bagherzadeh, "PLAM: A Posit Logarithm-Approximate Multiplier," in IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 4, pp. 2079-2085, 1 Oct.-Dec. 2022, doi: 10.1109/TETC.2021.3109127.2168-675010.1109/TETC.2021.3109127https://hdl.handle.net/20.500.14352/132729© 2022, IEEE PR2003_20/01The 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.engjournal articlehttps://doi.org/10.1109/TETC.2021.3109127https://ieeexplore.ieee.org/document/9530365https://arxiv.org/abs/2102.09262open access004.3621.38Posit arithmeticArithmetic and logic structuresLow-power designMachine learningComputer visionInformática (Informática)Hardware1203 Ciencia de Los Ordenadores3307.03 Diseño de Circuitos