LEARNING STATIC BOUND MANAGEMENT PARAMETERS FOR ANALOG RESISTIVE PROCESSING UNIT SYSTEM

Techniques are provided for learning static bound management parameters for an analog resistive processing unit system which is configured for neuromorphic computing. For example, a system comprises one or more processors which are configured to: perform a first training process to train a first art...

Full description

Saved in:
Bibliographic Details
Main Authors Le Gallo-Bourdeau, Manuel, Chen, An, Sasidharan Rajalekshmi, Nandakumar, Rasch, Malte Johannes, Mackin, Charles, Tsai, HsinYu
Format Patent
LanguageEnglish
Published 30.03.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
Online AccessGet full text

Cover

More Information
Summary:Techniques are provided for learning static bound management parameters for an analog resistive processing unit system which is configured for neuromorphic computing. For example, a system comprises one or more processors which are configured to: perform a first training process to train a first artificial neural network model; perform a second training process to retrain the first artificial neural network model using matrix-vector compute operations which are a function of bound management parameters of an analog resistive processing unit system, to thereby generate a second artificial neural network model with learned static bound management parameters; and configure the resistive processing unit system to implement the second artificial neural network model and the learned static bound management parameters.
Bibliography:Application Number: US202117485342