Presentation
CHiRM-DSE: CodeLLM-Based Hierarchical and Rule-Mining Guided DSE for FPGA Accelerators
DescriptionHigh-Level Synthesis (HLS) lowers the barrier to FPGA development by allowing a wider range of programmers to design hardware accelerators. However, determining the appropriate synthesis directives (pragmas) remains a major challenge, particularly for developers without hardware expertise. As designs grow more complex and the pragma search space expands, choosing the right pragmas becomes essential for achieving low resource usage and high performance. Automated design space exploration (DSE) provides an effective solution to this challenge. The enormous search space and time-consuming design-point evaluation highlight the need for efficient search strategies. However, existing search strategies mostly rely on inefficient exhaustive search, hyperparameter-sensitive metaheuristic methods, or dedicated methods that are difficult to port and generalize.
To address these issues, we propose a rule-mining-based search strategy that efficiently guides exploration toward the most promising regions of the design space. In addition, we introduce a design space decomposition method to prune the search space, as well as a CodeLLM-based design-point evaluation method, which is both faster than directly invoking HLS tools, and more accurate than prior GNN-based approaches. Experimental results on four widely-used HLS benchmarks demonstrate that, under the same time budget, our DSE framework achieves better Quality of Results (QoR) than the state of the art. Our demo code is released at https://github.com/ScopeHLS/CHiRM-DSE
To address these issues, we propose a rule-mining-based search strategy that efficiently guides exploration toward the most promising regions of the design space. In addition, we introduce a design space decomposition method to prune the search space, as well as a CodeLLM-based design-point evaluation method, which is both faster than directly invoking HLS tools, and more accurate than prior GNN-based approaches. Experimental results on four widely-used HLS benchmarks demonstrate that, under the same time budget, our DSE framework achieves better Quality of Results (QoR) than the state of the art. Our demo code is released at https://github.com/ScopeHLS/CHiRM-DSE
Event Type
Research Manuscript
TimeMonday, July 2710:30am - 10:43am PDT
LocationMtg Room 201B