Abstract: The disclosed embodiments relate to a system that generates and executes a deep neural network (DNN) based on target runtime parameters. During operation, the system receives a trained original model and a set of target runtime parameters for the DNN, wherein the target runtime parameters are associated with one or more of the following for the DNN: desired operating conditions, desired resource utilization, and desired accuracy of results. Next, the system generates a context-specific model based on the original model and the set of target runtime parameters. The system also generates an operational plan for executing both the original model and the context-specific model to meet requirements of the target runtime parameters. Finally, the system controls execution of the original model and the context-specific model based on the operational plan.

Abstract: The disclosed embodiments relate to a system that optimizes execution of a DNN based on operational performance parameters. During operation, the system collects the operational performance parameters from the DNN during operation of the DNN, wherein the operational performance parameters include parameters associated with operating conditions for the DNN, parameters associated with resource utilization during operation of the DNN, and parameters associated with accuracy of results produced by the DNN. Next, the system uses the operational performance parameters to update the DNN model to improve performance and efficiency during execution of the DNN.

Abstract: The disclosed embodiments relate to a system that generates and executes a deep neural network (DNN) based on target runtime parameters. During operation, the system receives a trained original model and a set of target runtime parameters for the DNN, wherein the target runtime parameters are associated with one or more of the following for the DNN: desired operating conditions, desired resource utilization, and desired accuracy of results. Next, the system generates a context-specific model based on the original model and the set of target runtime parameters. The system also generates an operational plan for executing both the original model and the context-specific model to meet requirements of the target runtime parameters. Finally, the system controls execution of the original model and the context-specific model based on the operational plan.

Abstract: The disclosed embodiments relate to a system that optimizes execution of a DNN based on operational performance parameters. During operation, the system collects the operational performance parameters from the DNN during operation of the DNN, wherein the operational performance parameters include parameters associated with operating conditions for the DNN, parameters associated with resource utilization during operation of the DNN, and parameters associated with accuracy of results produced by the DNN. Next, the system uses the operational performance parameters to update the DNN model to improve performance and efficiency during execution of the DNN.

Abstract: The disclosed embodiments relate to a system that facilitates dynamic runtime execution of a deep neural network (DNN). During operation, the system receives a model, a set of weights and runtime metadata for the DNN. The system also obtains code to perform inference-processing operations for the DNN. Next, the system compiles code to implement a runtime engine that facilitates throttling operations during execution of the inference-processing operations, wherein the runtime engine conserves computing resources by selecting portions of the inference-processing operations to execute based on the runtime metadata.

Abstract: An example system in accordance with an aspect of the present disclosure includes a framework to demonstrate and/or train at least one live product. The framework includes a first panel associated with at least one step, and a second panel to display content and at least a portion of the at least one live product according to the at least one step. The system also includes at least one script to perform at least one task associated with the at least one step. The at least one step is performable independent of an order in which the at least one step is presented by the framework.

Abstract: An example system in accordance with an aspect of the present disclosure includes a framework to demonstrate and/or train at least one live product. The framework includes a first panel associated with at least one step, and a second panel to display content and at least a portion of the at least one live product according to the at least one step. The system also includes at least one script to perform at least one task associated with the at least one step. The at least one step is performable independent of an order in which the at least one step is presented by the framework.