Logarithmic number system for deep learning

In this paper the logarithmic Number System (LNS) is adopted to implement Long-Short Term Memory (LSTM), the basic component of a deep learning network type. Initially, piece wise approximations to activation functions σ and tanh are proposed and evaluated in LNS. Secondly, LNS multipliers and adder...

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Bibliographic Details
Published in2018 7th International Conference on Modern Circuits and Systems Technologies (MOCAST) pp. 1 - 4
Main Authors Kouretas, I., Paliouras, V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2018
Subjects
Adders
Complexity theory
Computer architecture
deep-learning networks
Hardware
LNS
Logic gates
LSTM
Machine learning
Training
VLSI
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Summary:In this paper the logarithmic Number System (LNS) is adopted to implement Long-Short Term Memory (LSTM), the basic component of a deep learning network type. Initially, piece wise approximations to activation functions σ and tanh are proposed and evaluated in LNS. Secondly, LNS multipliers and adders are implemented for wordlengths of 9,10 and 11 bits. The circuits are implemented in an 90-nm 1.0 V CMOS standard-cell library and quantitative results are reported. Results demonstrate that LNS is a good candidate for data representation and processing in deep learning networks, as area reduction of up to 36% is possible.
DOI:10.1109/MOCAST.2018.8376572