Storage methods and devices with secure customer-updatable machine learning models

Abstract

A data storage device stores a set of pre-trained machine learning (ML) models along with corresponding customer digital certificates and other information. Techniques are provided herein for allowing customers to access selected ML models using a digital certificate and a public key so that the selected ML models may then be loaded into a data storage controller of the data storage device for use, for example, by firmware of the controller to intelligently control data pre-fetch, data relocation, or other data processing functions. Techniques are also provided to allow customers to selectively update or replace ML models with customer-supplied models, subject to authentication of the customer and verification of the compatibility of the ML model within the data storage controller. Still other techniques are provided for allowing ML models from the data storage device to be loaded into a secure enclave within the host to execute within the secure enclave.