Nvidia Corporation

Slawomir Kierat

Alex Fit-Florea

Hao Fang Wu

Ujval J Kapasi

Boris Ginsburg

Dilip Sequeira

Yilin Zhang

Amir Gholaminejad

Michael Houston

Szymon Migacz

Zacky Zhou

Sergei Nikolaev

Ahmad Kiswani

Maxim Milakov

Hao Wu

Aspects of the present invention are directed to computer-implemented techniques for improving the training of artificial neural networks using a reduced precision (e.g., float16) data format. Embodiments of the present invention rescale tensor values prior to performing matrix operations (such as matrix multiplication or matrix addition) to prevent overflow and underflow. To preserve accuracy throughout the performance of the matrix operations, the scale factors are defined using a novel data format to represent tensors, wherein a matrix is represented by the tuple X, where X=(a, v[.]), wherein a is a float scale factor and v[.] are scaled values stored in the float16 format. The value of any element X[i] according to this data format would be equal to a*v[i].

Tensor processing using low precision format

Aspects of the present invention are directed to computer-implemented techniques for performing data compression and conversion between data formats of varying degrees of precision, and more particularly for improving the inferencing (application) of artificial neural networks using a reduced precision (e.g., INT8) data format. Embodiments of the present invention generate candidate conversions of data output, then employ a relative measure of quality to identify the candidate conversion with the greatest accuracy (i.e., least divergence from the original higher precision values). The representation can be then be used during inference to perform computations on the resulting output data.

Automated methods for conversions to a lower precision data format

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Mountain View, CA, United States of America

Slawomir Kierat