Presentation
Adaptive QoS Optimization for SoC Performance Enhancement Using Dueling Double Deep Q-Networks
SessionSystem & Software Design
DescriptionThis work presents a reinforcement learning (RL) approach to optimize System-on-Chip (SoC) performance by automatically tuning Quality of Service (QoS) knobs. As SoC complexity increases, manual optimization of diverse QoS parameters across varying scenarios becomes intractable. To address this, we develop a simulation environment mimicking the SoC, where an RL agent explores the design space to identify optimal knob settings. We employ Deep Q-Networks (DQN) and enhancements (e.g., Double DQN, Dueling Networks) within a Markov Decision Process (MDP) framework, defining states, actions, and reward systems based on key metrics like throughput, latency, and power. Techniques such as Temporal Difference (TD) learning and Prioritized Experience Replay (PER) improve sample efficiency and convergence. Our framework rapidly evaluates configurations under different SoC architectures. Experimental results demonstrate the RL model's ability to discover high-performance, power-efficient QoS settings through training, significantly improving over manual methods. This work highlights RL's potential in automating SoC design optimization, offering a scalable solution for complex multi-master systems and paving the way for future design automation research.
Event Type
Engineering Presentation
TimeMonday, July 2711:00am - 11:15am PDT
LocationSeaside Ballroom B