公开(公告)号：US11216719B2

公开(公告)日：2022-01-04

申请号：US16009456

申请日：2018-06-15

Applicant: INTEL CORPORATION

Inventor： Somdeb Majumdar ,  Ron Banner ,  Marcel Nassar ,  Lior Storfer ,  Adnan Agbaria ,  Evren Tumer ,  Tristan Webb ,  Xin Wang

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/063

Abstract: Logic may quantize a primary neural network. Logic may generate, by a secondary neural network logic circuitry for a primary neural network logic circuitry, quantization parameters. The primary neural network logic circuitry may comprise a primary neural network with multiple layers trainable with an objective function. Each of the multiple layers of the primary neural network may comprise multiple tensors. The secondary neural network logic circuitry may comprise one or more secondary neural networks trainable with the objective function to output the quantization parameters to the tensors.

公开(公告)号：US20220035878A1

公开(公告)日：2022-02-03

申请号：US17505568

申请日：2021-10-19

Applicant: Intel Corporation

Inventor： Anthony Sarah ,  Daniel Cummings ,  Juan Pablo Munoz ,  Tristan Webb

IPC: G06F16/953

Abstract: The present disclosure is related to framework for automatically and efficiently finding machine learning (ML) architectures that are optimized to one or more specified performance metrics and/or hardware platforms. This framework provides ML architectures that are applicable to specified ML domains and are optimized for specified hardware platforms in significantly less time than could be done manually and in less time than existing ML model searching techniques. Furthermore, a user interface is provided that allows a user to search for different ML architectures based on modified search parameters, such as different hardware platform aspects and/or performance metrics. Other embodiments may be described and/or claimed.