Patent · US Active

Complexity optimization of trainable networks

US10997502B1 · kind B1 · utility

6Cited by

1References

20Claims

0Family size

Assignee

Cadence Design Systems, Inc. · US

Inventors

Raul Alejandro Casas · Doylestown, US
Samer Hijazi · Bethlehem, US
Piyush Kaul · New Delhi, IN
Rishi Kumar · New York, US
Xuehong Mao · San Jose, US
Christopher Rowen · Santa Cruz, US

Key dates

Filing date	Apr 13, 2017
Grant date	May 4, 2021
Priority date	—
Expiry date	Jun 30, 2039

Classification

Technology area (CPC G)Physics
CPC primaryG06N3/09
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Some embodiments perform, in a multi-layer neural network in a computing device, optimization of the multi-layer neural network, for example by making a convolutional change with a first plurality of convolutional filters, or by making a connection change of a first plurality of convolutional filters. In other embodiments, electronic design automation (EDA) systems, methods, and computer-readable media are presented for adding such a multi-layer neural network into an integrated circuit (IC) design.

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