A Scalable and Reproducible System-on-Chip Simulation for Reinforcement Learning

TL;DR

This paper introduces gym-ds3, a scalable and reproducible simulation environment for high-fidelity System-on-Chip applications, facilitating reinforcement learning research and system optimization.

Contribution

It presents a novel open environment for DSSoC simulation that supports hierarchical job scheduling and bridges to reinforcement learning research.

Findings

Successfully mimics real-world embedded systems performance

Handles continuous, rapid job injection in simulation

Demonstrates effective run-time performance across schedulers

Abstract

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.