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The Ethereal
Low-Precision Batch-Normalized Activations
February 27, 2017 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Benjamin Graham
arXiv ID
1702.08231
Category
cs.NE: Neural & Evolutionary
Cross-listed
cs.CV
Citations
9
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Artificial neural networks can be trained with relatively low-precision floating-point and fixed-point arithmetic, using between one and 16 bits. Previous works have focused on relatively wide-but-shallow, feed-forward networks. We introduce a quantization scheme that is compatible with training very deep neural networks. Quantizing the network activations in the middle of each batch-normalization module can greatly reduce the amount of memory and computational power needed, with little loss in accuracy.
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