Booth Encoding-Based Energy Efficient Multipliers for Deep Learning Systems
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada
Funder
Natural Sciences and Engineering Research Council (NSERC), Canada
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/8920/10138208/10005129.pdf?arnumber=10005129
Reference19 articles.
1. A Two-Stage Operand Trimming Approximate Logarithmic Multiplier
2. Design and Analysis of Energy-Efficient Dynamic Range Approximate Logarithmic Multipliers for Machine Learning
3. Computer Multiplication and Division Using Binary Logarithms
4. Efficient Mitchell’s Approximate Log Multipliers for Convolutional Neural Networks
5. Hybrid Low Radix Encoding-Based Approximate Booth Multipliers
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