Invention Grant
- Patent Title: Implementing neural networks in fixed point arithmetic computing systems
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Application No.: US16830922Application Date: 2020-03-26
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Publication No.: US11868864B2Publication Date: 2024-01-09
- Inventor: William John Gulland
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06F7/483 ; G06N3/063 ; G06F5/01 ; G06N3/08

Abstract:
Methods, systems, and computer storage media for implementing neural networks in fixed point arithmetic computing systems. In one aspect, a method includes the actions of receiving a request to process a neural network using a processing system that performs neural network computations using fixed point arithmetic; for each node of each layer of the neural network, determining a respective scaling value for the node from the respective set of floating point weight values for the node; and converting each floating point weight value of the node into a corresponding fixed point weight value using the respective scaling value for the node to generate a set of fixed point weight values for the node; and providing the sets of fixed point floating point weight values for the nodes to the processing system for use in processing inputs using the neural network.
Public/Granted literature
- US20200226445A1 IMPLEMENTING NEURAL NETWORKS IN FIXED POINT ARITHMETIC COMPUTING SYSTEMS Public/Granted day:2020-07-16
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