Hardware-Software Stitching Algorithm in Lightweight Q-Learning System on Chip (SoC) for Shortest Path Optimization

This paper presents a novel hardware-software co-deisgn approach to accelerate Q-learning algorithms using a RISC-V-based System-on-Chip (SoC) design. We introduce a maze-stitching algorithm that enables efficient solving of large, complex mazes by decomposing them into smaller sub-mazes and thus can perform Q-Learning computation in low-complexity hardware accelerator. Furthermore, we provide a comprehensive analysis of the algorithm’s complexity, theoretical performance gains, and practical implementation results. The proposed implementation demonstrates significant performance improvements over traditional software approaches, achieving speedups ranging from 84× to 233× for complex maze-solving tasks while maintaining a small footprint. The algorithm, even without the accelerator also achieved speedups ranging from 13× to 36×. The proposed system combines a 64-bit RISC-V core with a Q-Learning accelerator, operating at 50MHz on an Arty A7-100T FPGA. The maze-stitching technique allows for scaling to larger problem sizes while maintaining hardware efficiency. The proposed work can be applied to a lightweight accelerator and provides a scalable solution for resource-constrained edge computing environments.