🤖 AI Summary
High computational cost, slow convergence, and suboptimal results in high-level synthesis (HLS) design space exploration (DSE) stem from combinatorial explosion of optimization directives. Method: This paper proposes the first large language model (LLM)-driven DSE framework, which leverages LLMs’ semantic understanding of hardware design quality to enable intelligent pruning and calibrated initial sampling. The framework synergistically balances convergent and divergent thinking to jointly optimize solution quality and diversity. Contribution/Results: Compared to conventional heuristic methods, the framework achieves 5.1×–16.6× improvement in approximating the reference Pareto front. It attains equivalent multi-objective optimization performance using only 4.6% of the exploration overhead required by NSGA-II, significantly enhancing both efficiency and accuracy of multi-objective DSE in HLS.
📝 Abstract
High-Level Synthesis (HLS) serves as an agile hardware development tool that streamlines the circuit design by abstracting the register transfer level into behavioral descriptions, while allowing designers to customize the generated microarchitectures through optimization directives. However, the combinatorial explosion of possible directive configurations yields an intractable design space. Traditional design space exploration (DSE) methods, despite adopting heuristics or constructing predictive models to accelerate Pareto-optimal design acquisition, still suffer from prohibitive exploration costs and suboptimal results. Addressing these concerns, we introduce iDSE, the first LLM-aided DSE framework that leverages HLS design quality perception to effectively navigate the design space. iDSE intelligently pruns the design space to guide LLMs in calibrating representative initial sampling designs, expediting convergence toward the Pareto front. By exploiting the convergent and divergent thinking patterns inherent in LLMs for hardware optimization, iDSE achieves multi-path refinement of the design quality and diversity. Extensive experiments demonstrate that iDSE outperforms heuristic-based DSE methods by 5.1$ imes$$sim$16.6$ imes$ in proximity to the reference Pareto front, matching NSGA-II with only 4.6% of the explored designs. Our work demonstrates the transformative potential of LLMs in scalable and efficient HLS design optimization, offering new insights into multiobjective optimization challenges.