Abstract: Embodiments of the present disclosure relate to workload-based dynamic throttling of video processing functions. Systems and methods are disclosed that dynamically throttle video processing and/or streaming based on a workload. Live video is captured from one or more sources (e.g., cameras) and stored. The video is then provided to a video processing engine and a video streaming engine. The video processing engine may perform one or more operations such as object detection, object tracking, and object classification to produce characterization data (e.g., bounding boxes, object trajectories, alerts, object labels, object counts, boundary crossings, intersection highlighting, etc.). System resource usage and performance of the video processing and streaming are monitored to produce workload data (e.g., metrics). Based on the policies and the workload data, the video streaming and/or processing is dynamically reconfigured by adjusting parameters provided to the video streaming and processing engines.

Abstract: Embodiments of the present disclosure relate to workload-based dynamic throttling of video processing functions. Systems and methods are disclosed that dynamically throttle video processing and/or streaming based on a workload. Live video is captured from one or more sources (e.g., cameras) and stored. The video is then provided to a video processing engine and a video streaming engine. The video processing engine may perform one or more operations such as object detection, object tracking, and object classification to produce characterization data (e.g., bounding boxes, object trajectories, alerts, object labels, object counts, boundary crossings, intersection highlighting, etc.). System resource usage and performance of the video processing and streaming are monitored to produce workload data (e.g., metrics). Based on the policies and the workload data, the video streaming and/or processing is dynamically reconfigured by adjusting parameters provided to the video streaming and processing engines.

Abstract: An improved method and apparatus for performing program code conversion is provided and, more particularly, for generating improved intermediate representations for use in program code conversion. During program code conversion, a partial dead code elimination optimization technique is implemented to identify partially dead register definitions within a block of program code being translated. The partial dead code elimination is an optimization to the intermediate representation in the form of code motion for blocks of program code ending in non-computed branches or computed jumps, where target code for all dead child nodes of a partially dead register definition is prevented from being generated and target code for partially dead child nodes of a partially dead register definition is delayed from being generated until after target code is generated for all fully live child nodes for the partially dead register definition.

Abstract: An improved method and apparatus for performing program code conversion is provided and, more particularly, for generating improved intermediate representations for use in program code conversion. During program code conversion, a partial dead code elimination optimization technique is implemented to identify partially dead register definitions within a block of program code being translated. The partial dead code elimination is an optimization to the intermediate representation in the form of code motion for blocks of program code ending in non-computed branches or computed jumps, where target code for all dead child nodes of a partially dead register definition is prevented from being generated and target code for partially dead child nodes of a partially dead register definition is delayed from being generated until after target code is generated for all fully live child nodes for the partially dead register definition.