In particular embodiments, in response a data subject submitting a request to delete their personal data from an organization's systems, the system may: (1) automatically determine where the data subject's personal data is stored; (2) in response to determining the location of the data (which may be on multiple computing systems), automatically facilitate the deletion of the data subject's personal data from the various systems; and (3) determine a cause of the request to identify one or more processing activities or other sources that result in a high number of such requests.

Some embodiments provide a compiler for optimizing the implementation of a machine-trained network (e.g., a neural network) on an integrated circuit (IC). In some embodiments, the compiler determines whether sparsity requirements of channels implemented on individual cores are met on each core. If the sparsity requirement is not met, the compiler, in some embodiments, determines whether the channels of the filter can be rearranged to meet the sparsity requirements on each core and, based on the determination, either rearranges the filter channels or implements a solution to non-sparsity.

Some embodiments provide a compiler for optimizing the implementation of a machine-trained network (e.g., a neural network) on an integrated circuit (IC). In some embodiments, the compiler determines whether sparsity requirements of channels implemented on individual cores are met on each core. If the sparsity requirement is not met, the compiler, in some embodiments, determines whether the channels of the filter can be rearranged to meet the sparsity requirements on each core and, based on the determination, either rearranges the filter channels or implements a solution to non-sparsity.

Techniques for scalable virtualization of an Input/Output (I/O) device are described. An electronic device composes a virtual device comprising one or more assignable interface (AI) instances of a plurality of AI instances of a hosting function exposed by the I/O device. The electronic device emulates device resources of the I/O device via the virtual device. The electronic device intercepts a request from the guest pertaining to the virtual device, and determines whether the request from the guest is a fast-path operation to be passed directly to one of the one or more AI instances of the I/O device or a slow-path operation that is to be at least partially serviced via software executed by the electronic device. For a slow-path operation, the electronic device services the request at least partially via the software executed by the electronic device.

Techniques for scalable virtualization of an Input/Output (I/O) device are described. An electronic device composes a virtual device comprising one or more assignable interface (AI) instances of a plurality of AI instances of a hosting function exposed by the I/O device. The electronic device emulates device resources of the I/O device via the virtual device. The electronic device intercepts a request from the guest pertaining to the virtual device, and determines whether the request from the guest is a fast-path operation to be passed directly to one of the one or more AI instances of the I/O device or a slow-path operation that is to be at least partially serviced via software executed by the electronic device. For a slow-path operation, the electronic device services the request at least partially via the software executed by the electronic device.

A method and apparatus is disclosed for using supervised learning with closed loop feedback to improvement of output consistency for memory arrangements, such as a solid state drive.

A method and apparatus is disclosed for using supervised learning with closed loop feedback to improvement of output consistency for memory arrangements, such as a solid state drive.

A method for recommending a route includes obtaining a first start point and a first end point relating to a road network. The method also includes obtaining a route recommendation model. The method further includes determining a recommendation route from the first start point to the first end point based on the route recommendation model.

A method for recommending a route includes obtaining a first start point and a first end point relating to a road network. The method also includes obtaining a route recommendation model. The method further includes determining a recommendation route from the first start point to the first end point based on the route recommendation model.

Responding to a data subject access request includes receiving the request and identifying the requestor and source. In response to identifying the requestor and source, a computer processor determines whether the data subject access request is subject to fulfillment constraints, including whether the requestor or source is malicious. If so, then the computer processor denies the request or requests a processing fee prior to fulfillment. If not, then the computer processor fulfills the request.