Apparatus and method for dynamic microarchitecture adaption using machine learning to improve core performance

A processor, comprising:

• a plurality of microarchitectural circuits associated with processing instructions and data;
• machine learning circuitry configurable in accordance with a trained reinforcement learning model, the trained reinforcement learning model pre-trained based on a type of the processor;
• and
• telemetry collection circuitry to collect first telemetry data from the plurality of microarchitectural circuits or a subset thereof;
• wherein the machine learning circuitry operable in accordance with the trained reinforcement learning model is to determine microarchitectural configuration updates based on the first telemetry data and characteristics of at least one workload to be executed using one or more of the plurality of microarchitectural circuits.

A machine-readable medium having program code stored thereon which, when executed by a machine, causes the machine to perform operations, comprising:

• configuring a trained reinforcement learning model on a machine learning circuitry integral to a first processor of a first processor type, the trained reinforcement learning model having been trained with microarchitectural performance data and workload data corresponding to the first processor type;
• determining, by the machine learning circuitry using the trained reinforcement learning model, microarchitectural configuration updates based on first telemetry data and characteristics of workloads to be executed;
• and
• applying the microarchitectural configuration updates on the first processor.