A Scalable and Reproducible System-on-Chip Simulation for Reinforcement Learning
This provides a domain-specific simulation tool for reinforcement learning researchers working on embedded systems, but it is incremental as it extends existing interaction schemes.
The paper tackles the problem of simulating Domain-Specific System-on-Chip (DSSoC) applications for reinforcement learning by introducing gym-ds3, a scalable and reproducible environment that schedules hierarchical jobs onto heterogeneous processors, successfully mimicking results from standard frameworks and real-world systems.
Deep Reinforcement Learning (DRL) underlies in a simulated environment and optimizes objective goals. By extending the conventional interaction scheme, this paper proffers gym-ds3, a scalable and reproducible open environment tailored for a high-fidelity Domain-Specific System-on-Chip (DSSoC) application. The simulation corroborates to schedule hierarchical jobs onto heterogeneous System-on-Chip (SoC) processors and bridges the system to reinforcement learning research. We systematically analyze the representative SoC simulator and discuss the primary challenging aspects that the system (1) continuously generates indefinite jobs at a rapid injection rate, (2) optimizes complex objectives, and (3) operates in steady-state scheduling. We provide exemplary snippets and experimentally demonstrate the run-time performances on different schedulers that successfully mimic results achieved from the standard DS3 framework and real-world embedded systems.