Methods, systems, and devices for techniques for managing temporarily retired blocks of a memory system are described. In some examples, aspects of a memory system or memory device may be configured to determine an error for a block of memory cells. For example, a controller may determine an existence of the error and may temporarily retire the block. A media management operation may be performed on the temporarily retired block and, depending on one or more characteristics of the error, the temporarily retired block may be enabled or retired.

Various embodiments relate to a memory controller, including: a memory interface connected to a memory; an address and command logic connected to the memory interface and a command interface, wherein the address and control logic is configured to receive a memory read request; a memory scrubber configured to cycle through memory locations and to read data from those locations; a region selector configured to determine when a memory location read by the memory scrubber is within an integrity checked memory region; a runtime integrity check (RTIC) engine connected to a read data path of the memory interface, wherein the RTIC engine is configured to calculate an integrity check value for the RTIC region using data read from the checked memory region by the memory scrubber; and a RTIC controller configured to compare the calculated integrity check value for the checked memory region to a reference integrity check value for the checked memory region.

In one or more embodiments, the disclosed systems, methods, and media include utilizing a crosswalk algorithm to identify controls (e.g., cybersecurity controls) across frameworks, and for utilizing identified controls to generate cybersecurity risk assessments. A cybersecurity module may identify one or more controls in a data structure. The process may utilize a crosswalk algorithm to determine a relatedness between the identified controls and different controls of different frameworks. The process may update the data structure with selected different controls, such that a more robust set of controls are identified when the cybersecurity module indexes into the data structure to identify particular controls. Additionally, the process may generate a risk assessment for a device/software. The process may generate a risk score for the risk assessment, and the risk score may be based on a determined compliance level for each control determined to be related to a defined risk of interest.

Methods, systems, and computer-readable storage media for receiving, by an anomalous operation detection service, current signal data representing a driving current applied to a device over a time period, processing, by an anomalous operation detection service, the current signal data through a deep neural network (DNN) module, a frequency spectrum analysis (FSA) module, and a time series classifier (TSC) module to provide a set of indications, each indication in the set of indications indicating one of normal operation of the device and anomalous operation of the device, processing, by an anomalous operation detection service, the set of indications through a voting gate to provide an output indication, the output indication indicating one of normal operation of the device and anomalous operation of the device, and selectively transmitting one or more of an alert and a message based on the output indication.

Allocating processing activities among multiple computing devices can include identifying multiple computing activities of a computer-executable process and, for each computing activity identified, estimating in real time the computing resources needed. The identifying can be in response to detecting a computer-executable instruction executed by one multiple communicatively coupled computing devices, and the computer-executable instruction can be associate with the computer-executable process. A current condition and configuration of each of the computing devices can be determined in real time. For each computing device an effect induced by executing one or more of the plurality of activities can be predicted, the predicting based each computing device's current condition and configuration and performed by a machine learning model trained using data collected from prior real-time processing of example process activities. Based on the predicting, computing activities can be allocated in real time among the computing devices.

A system and method for offset protection data in a RAID array. A computer system comprises client computers and data storage arrays coupled to one another via a network. A data storage array utilizes solid-state drives and Flash memory cells for data storage. A storage controller within a data storage array is configured to store user data in a first page of a first storage device of the plurality of storage devices; generate intra-device protection data corresponding to the user data, and store the intra-device protection data at a first offset within the first page. The controller is further configured to generate inter-device protection data corresponding to the first page, and store the inter-device protection data at a second offset within a second page in a second storage device of the plurality of storage devices, wherein the first offset is different from the second offset.

Data recovery systems and methods utilize object-based storage for providing a data protection and recovery methodology with low recovery point objectives, and for enabling both full recovery and point-in-time based recovery. Data generated at a protected site (e.g., via one or more virtual machines) is intercepted during write procedures to primary storage. The intercepted data is replicated via a replication log, provided as data objects, and transmitted to an object based storage system. During recovery, data objects may be retrieved through point-in-time based recovery directly by the systems of the protected site, and/or data objects may be provided via full recovery, for example, within a runtime environment of a recovery site, with minimal data loss and operation interruption by rehydrating data objects within the runtime environment via low-latency data transfer and rehydration systems.

An integrated circuit (IC) test engine can generate a plurality of single cycle test patterns that target a plurality of static single cycle defects of a fabricated IC chip based on an IC design. The IC test engine can also fault simulate the plurality of single cycle test patterns against a plurality of multicycle defects in the IC design, wherein a given single cycle test pattern of the plurality of single cycle test patterns is sim-shifted to enable detection of a given multicycle fault and/or defect of the plurality of multicycle faults and/or defects.

Embodiments for managing real-time alerts using machine learning are disclosed. For example, a method includes receiving real-time data for one or more parameters of a device for which an alert is to be generated, from one or more sources associated with the device, and selecting a first machine learning model from a plurality of machine learning models based on the received real-time data. The method further includes determining at least one anomaly in the device based on the selected first machine learning model and predicting an impact of the determined at least one anomaly based on a second machine learning model of the plurality of machine learning models. Furthermore, the method includes generating the alert for the device in real-time based on the predicted impact of the determined at least one anomaly and receiving feedback on the generated alert in real-time.

In one embodiment, a method for margin determination for a computing system with a real time operating system and priority preemptive scheduling comprises: scheduling a set of tasks to be executed in one or more partitions, wherein each is assigned a priority, wherein the tasks comprise periodic and/or aperiodic tasks; executing the set of tasks on the computing system within the scheduled periodic time window; introducing an overhead task executed for an execution duration controlled either by the real time operating system or by the overhead task; controlling the overhead task to converge on a point of failure at which a length of the execution duration of the overhead task causes either: 1) a periodic task to fail to execute within a deadline, or 2) time available for the aperiodic tasks to execute to fall below a threshold; and defining a partition margin corresponding to the point of failure.