Runtime monitoring of machine learning-based scheduling algorithms toward robust domain-specific systems-on-chip

A system for runtime monitoring for a system-on-chip (SoC), the system comprising:

• at least one processing element (PE) configured to execute SoC application tasks;
• a memory operably coupled to the at least one PE;
• a runtime task scheduler implementing a machine learning (ML)-based policy for SoC application task scheduling;
• and
• a runtime monitor configured to:
• determine a scheduling action by the ML-based scheduler for an SoC application task,
• permit the scheduling action to be processed by the at least one PE,
• evaluate a quality of the scheduling action, and
• determine the scheduling action does not generalize to the policy based on the quality of the scheduling action.

A computer readable media comprising non-transitory computer executable instructions which, when executed by at least one processing element on a system-on-chip (SoC), perform at least:

• determining a scheduling action by the ML-based scheduler for an SoC task, the scheduling action based on a policy trained from training data;
• permitting the scheduling action to be processed by at least one processing element of the SoC;
• evaluating a quality of the scheduling action;
• and
• determining the scheduling action does not generalize to the policy based on the quality of the scheduling action.