Patent · US Active

Compiler for optimizing filter sparsity for neural network implementation configuration

US11625585B1 · kind B1 · utility

5Cited by

19References

18Claims

0Family size

Assignee

Perceive Corporation · US

Inventors

Brian Thomas · Fort Collins, US
Steven Teig · Menlo Park, US

Key dates

Filing date	Jul 29, 2019
Grant date	Apr 11, 2023
Priority date	—
Expiry date	Nov 7, 2041

Classification

Technology area (CPC G)Physics
CPC primaryG06N7/08
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Some embodiments provide a compiler for optimizing the implementation of a machine-trained network (e.g., a neural network) on an integrated circuit (IC). In some embodiments, the compiler determines whether sparsity requirements of channels implemented on individual cores are met on each core. If the sparsity requirement is not met, the compiler, in some embodiments, determines whether the channels of the filter can be rearranged to meet the sparsity requirements on each core and, based on the determination, either rearranges the filter channels or implements a solution to non-sparsity.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.