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PLAM: A Posit Logarithm-Approximate Multiplier
- Source :
- IEEE Transactions on Emerging Topics in Computing. 10:2079-2085
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
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.
- Subjects :
- FOS: Computer and information sciences
Scheme (programming language)
Computer Science - Machine Learning
Logarithm
Artificial neural network
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Machine Learning (cs.LG)
Computer Science Applications
Power (physics)
Human-Computer Interaction
Computer Science (miscellaneous)
Multiplication
Multiplier (economics)
Arithmetic
computer
Information Systems
computer.programming_language
Subjects
Details
- ISSN :
- 23764562
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Emerging Topics in Computing
- Accession number :
- edsair.doi.dedup.....204d45236a7b8e702c762f1edd31c1bf