Machine Learned Machines: Adaptive Co-optimization of Caches, Cores, and On-chip Network

Modern multicore architectures require runtime optimization techniques to address the problem of mismatches between the dynamic resource requirements of different processes and the runtime allocation. Choosing between multiple optimizations at runtime is complex due to the non-additive effects, making the adaptiveness of the machine learning techniques useful. We present a novel method, Machine Learned Machines (MLM), by using Online Reinforcement Learning (RL) to perform dynamic partitioning of the last level cache (LLC), along with dynamic voltage and frequency scaling (DVFS) of the core and uncore (interconnection network and LLC). We show that the co-optimization results in much lower energy-delay product (EDP) than any of the techniques applied individually. The results show an average of 19.6% EDP and 2.6% execution time improvement over the baseline.