Residual quantization for neural networks

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United States of America Patent

PATENT NO 11586883
APP PUB NO 20200193273A1
SERIAL NO

16221187

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Abstract

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Methods and apparatus are disclosed for providing emulation of quantized precision operations in a neural network. In some examples, the quantized precision operations are performed in a block floating-point format where values of a tensor share a common exponent. Techniques for selecting higher precision or lower precision can be used based on a variety of input metrics. When converting to a quantized tensor, a residual tensor is produced. In one embodiment, an error value associated with converting from a normal-precision floating point number to the quantized tensor is used to determine whether to use the residual tensor in a dot product calculation. Using the residual tensor increases the precision of an output from a node. Selection of whether to use the residual tensor can depend on various input metrics including the error value, the layer number, the exponent value, the layer type, etc.

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Patent Owner(s)

  • MICROSOFT TECHNOLOGY LICENSING, LLC.

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Chung, Eric S Woodinville, US 68 772
Lo, Daniel Bothell, US 37 474
Zhang, Jialiang Middleton, US 253 1145
Zhao, Ritchie Ithaca, US 14 157

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