QiMeng-EvoPartition: Rethinking the Impact of Partitioning for Automated Pipeline Design

摘要

As a key technique for improving throughput by increasing clock frequency and reducing Cycles per Instruction (CPI), pipeline design increasingly relies on automated methods with the growing scale of modern circuits. However, existing automated pipelining methods often decouple partitioning from CPI optimization, implicitly assuming that partitioning affects only clock frequency. We observe that different partitioning strategies significantly alter the pipeline stage differences between logic gates, thereby impacting the average number of stall cycles and ultimately affecting CPI. Thus, automated pipeline partitioning should jointly reduce CPI and improve clock frequency while ensuring functional correctness, resulting in a multi-objective optimization problem. To address this issue, we propose EvoPartition, an evolutionary partitioning framework for automated pipeline design. Specifically, EvoPartition first inserts virtual nodes to guarantee functional correctness. Then, through iterative evolution for reducing CPI and improving clock frequency, pipeline stage assignments of all nodes are determined. Experimental results on ISCAS85 and EPFL show that EvoPartition achieves a 7.34% CPI optimization by reducing stage differences by 16.23%, and further improves throughput by 7.22% compared to the state-of-the-art.