Name
Title | ||
|---|---|---|
A Cost-Efficient Iterative Truncated Logarithmic Multiplication for Convolutional Neural Networks |
Abstract | ||
|---|---|---|
This paper proposes a cost-efficient approximate logarithmic multiplication for convolutional neural networks (CNNs), where two truncated logarithmic multipliers are connected for error correction. The proposed iterative logarithmic multiplication achieves low and unbiased average error while the hardware cost is significantly reduced by utilizing the truncated Mitchell multiplier and approximating error terms from the first stage. The proposed design has error characteristics that are suitable for neural network inferences, and the experiments on contemporary CNNs show that the proposed multiplier does not cause significant degradation on accuracy compared to exact multiplication. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ARITH.2019.00029 | 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH) |
Keywords | Field | DocType |
approximate multiplier,convolutional neural network,logarithmic multiplication,Mitchell multiplier | Adder,Convolutional neural network,Computer science,Parallel computing,Algorithm,Error detection and correction,Multiplier (economics),Multiplication,Logarithm,Artificial neural network,Cost efficiency | Conference |
ISSN | ISBN | Citations |
1063-6889 | 978-1-7281-3367-6 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Hyun Kim | 1 | 92 | 21.61 |
| MIN SOO KIM | 2 | 82 | 16.71 |
| Alberto A. Del Barrio | 3 | 78 | 14.49 |
| Nader Bagherzadeh | 4 | 1674 | 182.54 |