Abstract: Techniques for generating and executing an execution plan for a machine learning (ML) model using one of an edge device and a non-edge device are described. In some examples, a request for the generation of the execution plan includes at least one objective for the execution of the ML model and the execution plan is generated based at least in part on comparative execution information and network latency information.

Abstract: Techniques for utilizing adaptable filters for edge-based deep learning models are described. Filters may be utilized by an edge electronic device to filter elements of an input data stream so that only a subset of the elements are used as inputs to a machine learning model run by the electronic device, enabling successful operation despite the input data stream potentially being generated at a higher rate than a rate in which the ML model can be executed. The filter can be a differential-type filter that generates difference representations between consecutive elements of the data stream to determine which elements are to be passed on for the ML model, a “smart” filter such as a neural network trained using outputs from the ML model allowing the filter to “learn” which elements are the most likely to be of value to be passed on, or a combination of both.

Abstract: Techniques for intelligent coalescing of media streams are described. A coalesce engine receives multiple media streams, such as audio or video streams, that are misaligned. The coalesce engine can analyze the media streams by comparing representations of elements of the media streams to detect the misalignment. The coalesce engine may determine an offset amount representing the misalignment, and if the offset amount meets or exceeds a threshold the coalesce engine can work to eliminate the misalignment by introducing one or more artificial delays before sending elements of ones of the media streams that are “ahead” of others of the streams. The coalese engine can additionally or alternatively send feedback to sources of the media streams, causing the source(s) to attempt to mitigate the misalignment.

Abstract: Techniques for generating and executing an execution plan for a machine learning (ML) model using one of an edge device and a non-edge device are described. In some examples, a request for the generation of the execution plan includes at least one objective for the execution of the ML model and the execution plan is generated based at least in part on comparative execution information and network latency information.

Abstract: A control system for use with a processor includes: (i) a controller configured to receive prediction information for a predicted temperature associated with the processor, and to determine a speed of operation for the processor based at least on a thermal model of the processor and the predicted temperature, where the speed supports an operational objective of the processor; and (ii) an error estimator that is separate from the controller, and that is configured to receive temperature information obtained from the processor operating at the speed, to determine updated prediction information based, at least in part, on the temperature information, and to output the updated prediction information to the controller.

Abstract: An example process for controlling a processor may include: (i) obtaining parameters associated with operation of a processor, where each of the parameters has a different time scale; (ii) performing an iterative process to identify ones of the parameters that achieve a particular energy efficiency in the processor, where the energy efficiency of the processor corresponds to a quasi-concave function having a maximum that corresponds to the ones of the parameters; and (iii) controlling the processor using the ones of the parameters.

Abstract: A control system for use with a processor includes: (i) a controller configured to receive prediction information for a predicted temperature associated with the processor, and to determine a speed of operation for the processor based at least on a thermal model of the processor and the predicted temperature, where the speed supports an operational objective of the processor; and (ii) an error estimator that is separate from the controller, and that is configured to receive temperature information obtained from the processor operating at the speed, to determine updated prediction information based, at least in part, on the temperature information, and to output the updated prediction information to the controller.

Abstract: An example process for controlling a processor may include: (i) obtaining parameters associated with operation of a processor, where each of the parameters has a different time scale; (ii) performing an iterative process to identify ones of the parameters that achieve a particular energy efficiency in the processor, where the energy efficiency of the processor corresponds to a quasi-concave function having a maximum that corresponds to the ones of the parameters; and (iii) controlling the processor using the ones of the parameters.