ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis

Abstract: The increasing complexity of computational demands has accelerated the adoption of domain-specific accelerators, yet traditional hardware design methodologies remain constrained by prolonged development and verification cycles. High-Level Synthesis (HLS) bridges the gap between software and hardware by enabling hardware design from high-level programming languages. However, its widespread adoption is hindered by strict coding constraints and intricate hardware-specific optimizations, creating significant obstacles for developers. Recent advancements in LLMs demonstrate substantial potential in hardware design automation. However, their effectiveness is limited by the scarcity of high-quality datasets, particularly in the context of HLS. To address these challenges, we introduce ChatHLS, an agile HLS design automation and optimization workflow that leverages fine-tuned LLMs integrated within a multi-agent framework for error correction and design optimization. Our extensive evaluations reveal that ChatHLS achieves an average repair pass rate of 82.7% over 612 test cases, outperforming the GPT-4o and Llama3-8B by 19.1% and 63.0%, respectively. Furthermore, ChatHLS delivers performance enhancements ranging from 1.9$\times$ to 14.8$\times$ upon resource-constrained kernels. By enabling sophisticated optimization reasoning within practical computational budgets, ChatHLS attains a 4.9$\times$ geometric mean speedup compared to state-of-the-art DSL-based approaches. These results underscore the potential of ChatHLS in substantially expediting hardware development cycles while maintaining rigorous standards of design reliability and optimization quality.