Abstract: Systems and methods are provided for self-calibrating a health state of a hardware resource using a Siamese network based on a plurality of feature variables. The feature variables may include hardware failure data, performance degradation data, and power consumption data. The hardware failure data is based on machine operation records and warranty logs. The performance degradation data is based on hourly performance data and a number of client requests for performing functions. The power consumption data uses power telemetry and a processor (e.g., CPU) usage. The present disclosure uses a Siamese network with a plurality of trained neural networks in parallel to determine a correlation between incident data and reference data (e.g., representing a hardware resource in a healthy state). Use of the Siamese network enables self-calibrating a health status of servers in a cloud system without imposing stress tests or complex computations to classify the respective servers.

Abstract: Systems, methods and media of optimization of temporary read errors (TRE) in three-dimensional (3D) NAND memory devices are disclosed. A disclosed memory device can comprises a plurality of memory cells arranged as an array of NAND memory strings, a plurality of word lines couple to the memory cells, and a controller. The controller is configured to determine whether a next read operation is a first read operation of the memory device after recovering from an idle state, and In response to a positive result of the determination, control the memory device to perform an extended pre-phase of the first read operation before a read-phase of the first read operation.