US 11,704,231 B2
Techniques for conformance testing computational operations
Barton Robert House, Jr., Fall City, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Jul. 26, 2019, as Appl. No. 16/523,732.
Prior Publication US 2021/0026759 A1, Jan. 28, 2021
Int. Cl. G06F 7/00 (2006.01); G06F 11/36 (2006.01); G06N 20/00 (2019.01); G06F 17/15 (2006.01); G06T 1/20 (2006.01); G06F 7/483 (2006.01)
CPC G06F 11/3688 (2013.01) [G06F 11/3692 (2013.01); G06F 17/15 (2013.01); G06N 20/00 (2019.01); G06T 1/20 (2013.01); G06F 7/483 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method for performing conformance testing of a machine learning (ML) computational operation configured to be implemented in a graphics processing unit (GPU) wherein the ML computational operation is performed by an algorithm specific to a hardware device, comprising:
generating, for the ML computational operation and based on one or more inputs, a reference result including one or more reference intermediate products and a reference accumulator output at a first level of precision, wherein the one or more reference intermediate products of the ML computational operation are within one or more ranges including a range where all values within the range are within a same single floating point exponent;
generating, for the ML computational operation and based on specifying, to the GPU, the one or more inputs and a second level of precision, a result for the hardware device including one or more hardware intermediate products and a hardware accumulator output using the GPU at the second level of precision; and
outputting a conformance result based on whether a variance between the reference result and the result is within a threshold range.