Abstract: 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.

Abstract: 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. A bag of machine learning models is used to reconstruct the data for the untested cylinders. A defect file for the HDD is generated, a classifier model may be applied to the defect file, and one or more neural network models may be applied. If the defects are unsuitable for use by the models, then a scan of the entire HDD is run instead. 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods are provided for deploying machine learning operations within existing storage devices for streamlining various calibration processes. Machine learning operations are specifically designed to generate inference data as a substitute for various measurements taken during calibration. These operations may be verified through additional sample measurements and rolled back when the results of the machine learning operations are outside of a range of approved values. Storage devices designed to utilize machine learning methods within calibration processes 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 an application stored in the non-volatile memory and receiving a request for measurement data from the application.

Abstract: Methods are provided for tactically deploying machine learning operations within existing storage devices without additional capital investment. Machine learning operations can be processed within a SoC of a storage device as embedded software. Storage device designed to utilize machine learning methods within existing configurations can include a non-volatile memory for storing data and 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 plurality of 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.

Abstract: The subject disclosure pertains to systems providing a smart visual comparison system, comprising a data compilation component that gathers control information relating to a first image and a second image, and a comparison component that identifies elements represented in the first and second image and compares the elements in the first image to elements in the second image. The system can compile the differences between elements and provide differences between the elements. The system can present only crucial differences to a user, resulting in an elegant comparison system. The user can input tolerance information to define crucial differences, to fit a particular case.