Machine learning supplemented storage device calibration

A method for calibrating a storage device based on machine-learning based inferences within a storage device, comprising:

• executing within the storage device an application configured to generate measurement data for each of a plurality of attributes associated with calibration of the storage device to account for variations unique to the particular storage device;
• generating first measurement data by internally measuring one attribute of the plurality of attributes;
• based on the first measurement data, dynamically generating within the storage device one or more thresholds corresponding to the one attribute;
• based on the first measurement data, internally generating inference data for remaining attributes of the plurality of attributes via an embedded machine-learning model;
• internally generating second measurement data by measuring a computationally-inexpensive attribute from the remaining attributes of the plurality of attributes;
• based at least in part on the dynamically-generated one or more thresholds, internally determining whether inference data for the computationally-inexpensive attribute corresponds to the second measurement data;
• and
• responsive to the inference data for the computationally-inexpensive attribute corresponding to the second measurement data, utilizing the inference data for the remaining attributes in place of the measurement data to avoid use of resources associated with directly obtaining the measurement data;
• wherein each of the measurement data and the inference data is utilized to include generation of calibration data corresponding to a bit length corresponding to a change of magnetic flux over a given area of a track of a recording disk surface of a hard disk drive storage device, corresponding to a particular head configured to operate over the recording disk surface, to minimize the area required to store a bit on the recording disk surface.

A storage device, comprising:

• means for storing data and executable instructions;
• and
• one or more processors communicatively coupled to the means for storing, the one or more processors being configured, individually or in combination, to direct the storage device to:
• receive a request from an application process for measurement of a plurality of attributes associated with calibration of hard disk drive storage device formatting to account for physical variations unique to the particular hard disk drive storage device;
• internally generate first measurement data by measuring at least one attribute of the plurality of attributes;
• based on the first measurement data, dynamically generate within the storage device one or more thresholds corresponding to the at least one attribute;
• based on the first measurement data, internally generate inference data for remaining attributes of the plurality of attributes via an embedded machine-learning model;
• internally generate second measurement data by measuring a computationally-inexpensive attribute from the remaining attributes of the plurality of attributes;
• based at least in part on the dynamically-generated one or more thresholds, internally determine whether inference data for the computationally-inexpensive attribute corresponds to the second measurement data;
• and
• responsive to the inference data for the computationally-inexpensive attribute corresponding to the second measurement data, utilize the inference data for the remaining attributes in place of measurement data to avoid use of resources associated with directly obtaining measurement data to generate calibration data corresponding to a flux change per inch over a given track of a recording disk surface of a hard disk drive storage device to minimize the area required to store a bit on the recording disk surface.