Abstract: Various embodiments may include methods executed by processors of computing devices for geometry based work execution prioritization. The processor may receive events, such as images. The processor may overlay a boundary shape on the event to identify discard regions of the event lying outside the boundary shape. The processor may identify work regions of the events lying within the working boundary shape. The devices may determine a cancellation likelihood for each of the identified work regions of the events. The processor may assign a trimming weight to each of the identified work regions based on the determined cancellation likelihoods. The processor may then add each of the identified work regions as a work item to an execution work list in an order based on the assigned trimming weights. The work items may be processed in order of trimming weight priority.

Abstract: Various embodiments provide methods, devices, and non-transitory processor-readable storage media enabling joint goals, such as joint power and performance goals, to be realized on a per heterogeneous processing device basis for heterogeneous parallel computing constructs. Various embodiments may enable assignments of power states for heterogeneous processing devices on a per heterogeneous processing device basis to satisfy an overall goal on the heterogeneous processing construct. Various embodiments may enable dynamic adjustment of power states for heterogeneous processing devices on a per heterogeneous processing device basis.

Abstract: Aspects of the present disclosure are directed to detecting and responding to injected faults. In some examples, fault injections are detected in a pipeline processor using transactional memory by comparing a predicted value (e.g. from a Value Predictor) against a subsequently loaded or computed reference value, and then detecting the fault based on the result of the comparison. If the predicted value is found to differ from the subsequently loaded or calculated value, the difference is deemed to be due to a fault and actions are taken to address the fault, such as by using deception or blinding of observable values. In some examples, the Value Predictor is modified to perform the comparison to detect the fault. The Value Predictor then notifies Transactional Hardware, which responds to the fault. In other examples described herein, the Value Predictor is unchanged and the Transactional Hardware detects and corrects the fault.

Abstract: Various embodiments may include methods executed by processors of computing devices for geometry based work execution prioritization. The processor may receive events, such as images. The processor may overlay a boundary shape on the event to identify discard regions of the event lying outside the boundary shape. The processor may identify work regions of the events lying within the working boundary shape. The devices may determine a cancellation likelihood for each of the identified work regions of the events. The processor may assign a trimming weight to each of the identified work regions based on the determined cancellation likelihoods. The processor may then add each of the identified work regions as a work item to an execution work list in an order based on the assigned trimming weights. The work items may be processed in order of trimming weight priority.

Abstract: Various embodiments may include methods executed by processors of computing devices for geometry based work execution prioritization of irregular shapes on a computing device. Various embodiments may include calculating cost functions for an irregularly shaped work region detected by the computing device. The processor may map the irregularly shaped work region to a geometrically-bounded first work region within an N-dimensional space. The processor may then assess the efficacy of implementing modification strategies such as merging work regions or splitting a large work region into sections. Two or more smaller work regions may be merged to create a larger work region that may be more easily processed by a processing unit. Similarly, large shapes may be split into multiple smaller regularly shaped work regions that may be processed by different processors.

Abstract: Various embodiments provide methods, devices, and non-transitory processor-readable storage media enabling joint goals, such as joint power and performance goals, to be realized on a per heterogeneous processing device basis for heterogeneous parallel computing constructs. Various embodiments may enable assignments of power states for heterogeneous processing devices on a per heterogeneous processing device basis to satisfy an overall goal on the heterogeneous processing construct. Various embodiments may enable dynamic adjustment of power states for heterogeneous processing devices on a per heterogeneous processing device basis.

Abstract: Various embodiments may include methods executed by processors of computing devices for geometry based work execution prioritization. The processor may receive events, such as images. The processor may overlay a boundary shape on the event to identify discard regions of the event lying outside the boundary shape. The processor may identify work regions of the events lying within the working boundary shape. The devices may determine a cancellation likelihood for each of the identified work regions of the events. The processor may assign a trimming weight to each of the identified work regions based on the determined cancellation likelihoods. The processor may then add each of the identified work regions as a work item to an execution work list in an order based on the assigned trimming weights. The work items may be processed in order of trimming weight priority.

Abstract: Various embodiments may include methods executed by processors of computing devices for geometry based work execution prioritization. The processor may receive events, such as images. The processor may overlay a boundary shape on the event to identify discard regions of the event lying outside the boundary shape. The processor may identify work regions of the events lying within the working boundary shape. The devices may determine a cancellation likelihood for each of the identified work regions of the events. The processor may assign a trimming weight to each of the identified work regions based on the determined cancellation likelihoods. The processor may then add each of the identified work regions as a work item to an execution work list in an order based on the assigned trimming weights. The work items may be processed in order of trimming weight priority.