GAE-LCT: A Run-Time GA-Based Classifier Evolution Method for Hardware LCT Controlled SoC Performance-Power Optimization

Learning classifier tables (LCTs) are classifier based and lightweight hardware reinforcement learning building blocks which inherit the concepts of learning classifier systems. LCTs are used as a per-core low level controllers to learn and optimize potentially conflicting objectives e.g. achieving a performance target under a power budget. A supervisor is used at the system level which translate system and application requirements into objectives for the LCTs. The classifier population in the LCTs has to be evolved in run-time to adapt to the changes in the mode, performance targets, constraints or workload being executed. Towards this goal, we present GAE-LCT, a genetic algorithm (GA) based classifier evolution for hardware learning classifier tables. The GA uses accuracy to evolve classifiers in run-time. We introduce extensions to the LCT to enable accuracy based genetic algorithm. The GA runs as a software process on one of the cores and interacts with the hardware LCT via interrupts. We evaluate our work using DVFS on an FPGA using Leon3 cores. We demonstrate GAE-LCT’s ability to generate accurate classifiers in run-time from scratch. GAE-LCT achieves 5% lower difference to IPS reference and 51.5% lower power budget overshoot compared to Q-table while requiring 75% less memory. The hybrid GAE-LCT also requires 12 times less software overhead compared to a full software implementation.