From Code to RTL: A Methodology for Analyzing the HLS Hardware Generation Process
Resumo
High-Level Synthesis (HLS) abstracts the hardware accelerator design process, but evaluating the generated RTL often becomes a “black-box” challenge for developers. To address this issue, we propose a systematic methodology to track and analyze the translation of C/C++ algorithms into hardware (RTL). Our approach integrates synthesis via Xilinx Vitis/Vivado 2024.2 with datapath mapping using Intel Quartus and behavioral waveform validation via ModelSim. By applying this methodology to different algorithms, we mapped how the compiler translates constant complexity codes into linear pipelines and iterative logic into multi-stage Finite State Machines (FSMs). To evaluate the architectural results extracted by our method, we conducted performance evaluations on a PYNQ-Z2 board. Experimental results show that while the hardware reduces clock cycles, the absolute speedup is constrained by communication overhead. We conclude that the proposed methodology provides a replicable pathway for developers to understand the HLS-to-RTL mapping.Referências
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Cong, J., Lau, J., Liu, G., Neuendorffer, S., Pan, P., Vissers, K., and Zhang, Z. (2022). Fpga hls today: Successes, challenges, and opportunities. ACM Trans. Reconfigurable Technol. Syst., 15(4).
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Khan, M. Z. S., Azar, K., and Kamali, H. (2026). Bench4hls: End-to-end evaluation of llms in high-level synthesis code generation. In 2026 Design, Automation Test in Europe Conference (DATE), pages 1–7.
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Vaithianathan, M., Patil, M., Ng, S. F., and Udkar, S. (2023). Comparative study of fpga and gpu for high-performance computing and ai. ESP International Journal of Advancements in Computational Technology (ESP-IJACT), 1(1):37–46.
Xiong, C., Liu, C., Li, H., and Li, X. (2025). Hlspilot: Llm-based high-level synthesis. In Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD ’24, New York, NY, USA. Association for Computing Machinery.
Xu, S., Jiang, Z., Zhang, Y., Boland, D., Bao, Y., and Shi, K. (2025). Hercules: Efficient verification of high-level synthesis designs with fpga acceleration. In Proceedings of the 2025 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, FPGA ’25, page 56–66, New York, NY, USA. Association for Computing Machinery.
Advanced Micro Devices, Inc. (2026). Vitis High-Level Synthesis User Guide (UG1399). AMD, San Jose, CA. v2025.2.
Alizadeh, B. and Shiroei, M. (2023). Automatic correction of rtl designs using a lightweight partial high level synthesis. Integration, 91:173–181.
ARM Limited (2016). SoC Designer AXI4 Protocol Bundle User Guide.
Carrion Schafer, B. and Sathe, C. (2025). From high-level synthesis lite to high-level synthesis full: Unlocking hls tool limitations. ACM Trans. Des. Autom. Electron. Syst., 31(2).
Cong, J., Lau, J., Liu, G., Neuendorffer, S., Pan, P., Vissers, K., and Zhang, Z. (2022). Fpga hls today: Successes, challenges, and opportunities. ACM Trans. Reconfigurable Technol. Syst., 15(4).
Curzel, S. (2024). Modern High-Level Synthesis: Improving Productivity with a Multi-level Approach, pages 15–25. Springer Nature Switzerland, Cham.
Herklotz, Y., Du, Z., Ramanathan, N., and Wickerson, J. (2021). An empirical study of the reliability of high-level synthesis tools. In 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pages 219–223.
Khan, M. Z. S., Azar, K., and Kamali, H. (2026). Bench4hls: End-to-end evaluation of llms in high-level synthesis code generation. In 2026 Design, Automation Test in Europe Conference (DATE), pages 1–7.
Koronis, J., Garnica, O., Ignacio Hidalgo, J., and Lanchares Dávila, J. (2025). Reverse-engineering optimization techniques of high-level synthesis: Practical insights into accelerating applications with amd-xilinx vitis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 44(7):2558–2570.
Lahti, S. and Hämäläinen, T. D. (2025). High-level synthesis for fpgas—a hardware engineer’s perspective. IEEE Access, 13:28574–28593.
Molina, R. S., Gil-Costa, V., Crespo, M. L., and Ramponi, G. (2022). High-level synthesis hardware design for fpga-based accelerators: Models, methodologies, and frameworks. IEEE Access, 10:90429–90455.
Vaithianathan, M., Patil, M., Ng, S. F., and Udkar, S. (2023). Comparative study of fpga and gpu for high-performance computing and ai. ESP International Journal of Advancements in Computational Technology (ESP-IJACT), 1(1):37–46.
Xiong, C., Liu, C., Li, H., and Li, X. (2025). Hlspilot: Llm-based high-level synthesis. In Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD ’24, New York, NY, USA. Association for Computing Machinery.
Xu, S., Jiang, Z., Zhang, Y., Boland, D., Bao, Y., and Shi, K. (2025). Hercules: Efficient verification of high-level synthesis designs with fpga acceleration. In Proceedings of the 2025 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, FPGA ’25, page 56–66, New York, NY, USA. Association for Computing Machinery.
Publicado
03/11/2026
Como Citar
GIMENEZ, Henrique Gregory; MIDORIKAWA, Edson Toshimi; ALMEIDA, Felipe Valencia de; SATO, Liria Matsumoto.
From Code to RTL: A Methodology for Analyzing the HLS Hardware Generation Process. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 27. , 2026, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 126-139.
DOI: https://doi.org/10.5753/sscad.2026.34690.
