Micro-Armed Bandit: Lightweight & Reusable Reinforcement Learning for Microarchitecture Decision-Making

Online Reinforcement Learning (RL) has been adopted as an effective mechanism in various decision-making problems in microarchitecture. Its high adaptability and the ability to learn at runtime are attractive characteristics in microarchitecture settings. However, although hardware RL agents are effective, they suffer from two main problems. First, they have high complexity and storage overhead. This complexity stems from decomposing the environment into a large number of states and then, for each of these states, bookkeeping many action values. Second, many RL agents are engineered for a specific application and are not reusable.

In this work, we tackle both of these shortcomings by designing an RL agent that is both lightweight and reusable across different microarchitecture decision-making problems. We find that, in some of these problems, only a small fraction of the action space is useful in a given time window. We refer to this property as temporal homogeneity in the action space. Motivated by this property, we design an RL agent based on Multi-Armed Bandit algorithms, the simplest form of RL. We call our agent Micro-Armed Bandit.

We showcase our agent in two use cases: data prefetching and instruction fetch in simultaneous multithreaded (SMT) processors. For prefetching, our agent outperforms non-RL prefetchers Bingo and MLOP by 2.6% and 2.3% (geometric mean), respectively, and attains similar performance as the state-of-the-art RL prefetcher Pythia-with the dramatically lower storage requirement of only 100 bytes. For SMT instruction fetch, our agent outperforms the Hill Climbing method by 2.2% (geometric mean).