Invention Grant
- Patent Title: Training neural network accelerators using mixed precision data formats
-
Application No.: US16223603Application Date: 2018-12-18
-
Publication No.: US11676003B2Publication Date: 2023-06-13
- Inventor: Bita Darvish Rouhani , Taesik Na , Eric S. Chung , Daniel Lo , Douglas C. Burger
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Agency: Klarquist Sparkman, LLP
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06N3/063 ; G06F17/15 ; G06F17/16 ; G06N3/084

Abstract:
Technology related to training a neural network accelerator using mixed precision data formats is disclosed. In one example of the disclosed technology, a neural network accelerator is configured to accelerate a given layer of a multi-layer neural network. An input tensor for the given layer can be converted from a normal-precision floating-point format to a quantized-precision floating-point format. A tensor operation can be performed using the converted input tensor. A result of the tensor operation can be converted from the block floating-point format to the normal-precision floating-point format. The converted result can be used to generate an output tensor of the layer of the neural network, where the output tensor is in normal-precision floating-point format.
Information query