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About Binaryconnect: Training Deep Neural Networks With Binary Weights During Propagations
This document introduces BinaryConnect, a method for training deep neural networks with binary weights during forward and backward propagation. Binary weights, which are constrained to only two possible values like -1 or 1, provide benefits for specialized deep learning hardware by replacing multiply-accumulate operations with simple additions and subtractions. The key aspects that make BinaryConnect workable are maintaining sufficient precision when accumulating gradients to average stochastic gradients, and that noisy weights can act as a form of regularization similar to dropout. Experimental results show BinaryConnect achieves near state-of-the-art performance on MNIST, CIFAR-10 and SVHN datasets.
Detailed Information
| Author: | ['Chanon Tonmai'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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