Western Digital Technologies, Inc.

Amit Chattopadhyay

Jonathan Lloyd

Chun Sei Tsai

Stella Achtenberg

Ofir Pele

Anand Gupta

Krzysztof Gladysz

Anand Lallan Gupta

Saket Giri

Kameron Jung

Methods are provided for tactically deploying machine learning operations within existing storage devices without the need for additional capital investment. Machine learning operations are specifically designed to locate and evaluate multiple types of data to complete an operation, including synthesizing missing data. These operations may be processed within a SoC of a storage device as embedded software. Storage devices designed to utilize machine learning methods within existing configurations can include a non-volatile memory for storing data, executable instructions, and a processor to conduct a variety of steps. The steps can include executing a plurality of applications stored in the non-volatile memory, and receiving a request for data, including measurements, from at least one of the applications. The steps can further determine if the requested data is suitable for substitution by an inference and subsequently select at least one machine learning model for generating a suitable inference.

Embedded multi-attribute machine learning for storage devices

Methods are provided for managing defects in Hard Disk Drive (HDD) storage devices. In particular, only a portion of the cylinders of an HDD is tested. Machine learning modeling is used to reconstruct the data for the untested cylinders. An HDD comprises a rotating disk and a read/write head actuated above the disk surface. The disk may be formatted into concentric data tracks, with each track being divided into sectors. The tracks may be organized into zones (groups of tracks called cylinders), and the axially parallel sectors in each cylinder may be organized into wedges. In a test mode, some portion of the cylinders is chosen for testing. Each wedge in the chosen cylinders is tested and labeled defective or non-defective. The test data for each defective wedge is run through a machine learning defect management logic, and inferences are made for the defective/non-defective status of the untested wedges.

Machine learning defect management in storage devices

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San Jose, CA, United States of America

Amit Chattopadhyay