Efficient Detailed Routing for FPGA Back-End Flow Using Reinforcement Learning

Abstract

Over the past few years, the computation capability of field-programmable gate arrays (FPGAs) has increased tremendously. This has led to the increase in the complexity of the designs implemented on FPGAs and to the time taken by the FPGA back-end flow. The FPGA back-end flow comprises of many steps, and routing is one of the most critical steps among them. Routing normally constitutes more than 50% of the total time taken by the back-end flow and an optimization at this step can lead to overall optimization of the back-end flow. In this work, we propose enhancements to the routing step by incorporating a reinforcement learning (RL)-based framework. In the proposed RL-based framework, we use the ϵ-greedy approach and customized reward functions to speed up the routing step while maintaining similar or better quality of results (QoR) as compared to the conventional negotiation-based congestion-driven routing solution. For experimentation, we use two sets of widely deployed, large heterogeneous benchmarks. Our results show that, for the RL-based framework, the ϵ-greedy greedy approach combined with a modified reward function gives better results as compared to purely greedy or exploratory approaches. Moreover, the incorporation of the proposed reward function in the RL-based framework and its comparison with a conventional routing algorithm shows that the proposed enhancement requires less routing time while giving similar or better QoR. On average, a speedup of 35% is recorded for the proposed routing enhancement as compared to negotiation-based congestion-driven routing solutions. Finally, the speedup of the routing step leads to an overall reduction in the execution time of the back-end flow of 25%.

Unraveling the Integration of Deep Machine Learning in FPGA CAD Flow: A Concise Survey and Future Insights

“…Farooq [15] proposed a reinforcement learning (RL)-based approach to the routing problem by transforming the classical routing iterative process into the training process of RL. The proposed method utilizes a greedy approach and customized reward functions to speed up the routing step while maintaining similar or better quality of results (QoR) compared to conventional congestion-driven routing solutions based on negotiation.…”

A Field Programmable Gate Array Placement Methodology for Netlist-Level Circuits with GPU Acceleration

“…Presently, commercial FPGA placement solutions predominantly rely on analytical methods and are recognized for their superior overall performance. In recent years, within the realm of large-scale digital integrated circuit placement research, placement tools based on machine learning methods have begun to emerge [21,22]. These include Google's EDA achievements based on reinforcement learning [23], the RLPlace placement tool based on reinforcement learning [24], and the DREAMPlace and DREAMPlaceFPGA frameworks with GPU acceleration [25,26].…”