iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs

• URL: http://arxiv.org/abs/2505.22086v2
• Date: Sat, 31 May 2025 11:52:20 GMT
• Title: iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs
• Authors: Runkai Li, Jia Xiong, Xi Wang,
• Abstract summary: High-Level Synthesis serves as an agile hardware development tool.<n>Traditional design space exploration (DSE) methods still suffer from prohibitive exploration costs and suboptimal results.<n>We introduce iDSE, the first LLM-aided DSE framework that leverages design quality perception to effectively navigate the design space.
• Score: 3.578537533079004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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$\times$$\sim$16.6$\times$ 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.

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