SOSA: Self-Optimizing Learning with Self-Adaptive Control for Hierarchical System-on-chip Management
Authors: Bryan Donyanavard, Tiago Muck, Amir M. Rahmani, Nikil Dutt, Armin Sadighi, Florian Mauer, Andreas Herkersdorf Venue: MICRO 2019 This work presents a control theory / reinforcement learning hybrid approach to solve online parameter tuning for SoC's called SOSA. While controllers are typically known for being light weight, and RL expensive, the authors build the hierarchy opposite from what you might expect. The RL models, Learning Classifier Tables (LCTs), are used as low-level controllers, and high level supervisor controller uses Supervisory Control Theory (SCT). The SCT controls a high-level system model abstraction, which must be consistent with the low-level system "as defined in the Ramadge-Wonham control mechanism" [1]. This assumption requires further investigation. LCTs are a simpler RL algorithm compared to today's deep neural network approaches. They utilize rule-based learning to to target an objective function, which may be multi-variate. The...