Sandisk Technologies Inc.

Western Digital Technologies, Inc.

Shaheed Nehal A

Lovish Singla

Ramkumar Ramamurthy

Prathmesh Tandon

Lovleen Arora

A data storage device and method are disclosed for providing external-interrupt-based customized behavior. In one embodiment, a data storage device is provided comprising a memory and a controller configured to communicate with the memory. The controller is further configured to: receive an interrupt from a host indicating that a user is experiencing a performance problem with the data storage device; and in response to receiving the interrupt, take an action to address an issue in the data storage device that is causing the performance problem. Other embodiments are disclosed.

Data storage device and method for providing external-interrupt-based customized behavior

Systems, video cameras, and methods for predictive adjustment of multi-camera surveillance video data capture based on a network of sub-region graph maps are described. Different groups of networked video cameras are deployed across a region and represented in sub-region graph maps based on the physical locations of the video cameras and interrelated through a master graph map. Each group of networked video cameras includes a master video camera able to connect directly and/or through a control center to the master video cameras in other groups to share video capture update messages in response to regional video alert events.

Predictive adjustment of distributed surveillance video data capture using networks of graph maps

A controller of a data storage device manages access to sequential streams based on predicted commands from a host device. When a command is received, the controller analyzes command characteristics associated with the command. The controller also analyzes command characteristics associated with each predicted command. The controller compares the command characteristics to determine whether any of the predicted commands are sequential to the received command. If the controller determines that one or more of the predicted commands are sequential to the received command, the controller enables data associated with the received command to be written to a sequential stream. The controller also updates a stream counter associated with the sequential stream. The stream counter is used to determine which sequential stream will be closed and subsequently opened when a new set of sequential commands is received or identified.

Managing streams based on predicted host commands

Systems, video cameras, and methods for predictive adjustment of multi-camera surveillance video data capture based on graph maps are described. A plurality of networked video camera is deployed and represented in a graph map based on the video camera environment, with parent nodes corresponding to video cameras and child nodes corresponding to path intersections among the video cameras. When a video event is detected from video data for one of the video cameras, a video capture update message indicating a shared child node identifier is selectively sent to other video cameras to modify their video capture operations.

Predictive adjustment of multi-camera surveillance video data capture using graph maps

A storage device performs a format operation for host devices using different format times and commands configurations. When a controller on the storage device receives an erase command from a host device, the controller determines the format time and a chunk size associated with data in the erase command. The controller executes a first format operation scheme, a second format operation scheme, or a third format operation scheme to perform an erase operation on the data in the erase command within the format time. The controller halts execution of the erase operation and returns operation to the host device when the format time expires.

Time bound partial format operation in a storage device

A method and apparatus for sustaining performance of a data storage device by predictively determining resource needs and executing processes to meet those needs before the resources are actually needed. According to certain embodiments, a controller collects commands coming from a host and provides these to a machine learning model such as a recurrent neural network (RNN). The RNN is trained using this data, and output of the trained model is used to predict future commands. As future commands are developed by the RNN, resource allocation processes such as garbage collection may be initiated prior to the actual need, during times when processing cycles in the data storage device are available. By operating the garbage collection when the device has available processing may mitigate transition to an urgent mode.

Growing community of inventors

Bengaluru, India

Shaheed Nehal A