PLAM: a Posit Logarithm-Approximate Multiplier
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 18 Feb 2021.
Builds on
8 of 35 references.
References · 8
- 2016Identity Mappings in Deep Residual Networkscited by 1,960
- 2018Quantizing deep convolutional networks for efficient inference: A whitepapercited by 507
- 2018Benchmark Analysis of Representative Deep Neural Network Architecturescited by 77
- 2018Rethinking floating point for deep learningcited by 22
- 2019QPyTorch: A Low-Precision Arithmetic Simulation Frameworkcited by 21
- 2018Deep Positron: A Deep Neural Network Using the Posit Number Systemcited by 13
- 2020The Effects of Approximate Multiplication on Convolutional Neural Networkscited by 10
- 2019Deep Learning Training on the Edge with Low-Precision Positscited by 4
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Citing papers per year: 2023: 3.
3 papers.