Intel Corporation

Tristan Webb

Somdeb Majumdar

Anthony Sarah

Daniel Cummings

Adnan M Agbaria

Lior Storfer

Marcel Nassar

Xin Wang

Juan Pablo Munoz

Ron Banner

Evren Tumer

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.

Framework for optimization of machine learning architectures

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.

Methods and arrangements to quantize a neural network with machine learning

Growing community of inventors

San Diego, CA, United States of America

Tristan Webb