Open-Source Hardware Design: From High-Level Code to Silicon with Bambu and SODA

DATE 2026 – Wednesday, April 22, 2026

Serena Curzel (Politecnico di Milano), Nicolas Bohm Agostnini (PNNL),

Fabrizio Ferrandi (Politecnico di Milano), Antonino Tumeo (PNNL)

Abstract

The open-source hardware design ecosystem has matured significantly in recent years, driven by a decentralized model that fosters collaboration and innovation. This evolution has enabled the integration of open-source Electronic Design Automation (EDA) tools, methodologies, and Process Design Kits (PDKs), supporting complete design flows for multiple technology nodes and diverse application domains. As modern applications ranging from AI to data analytics and beyond demand domain-specific accelerators to reach stringent performance and energy efficiency targets, open-source design environments are becoming essential to shorten design cycles, reduce costs, and foster innovation

This tutorial focuses on open-source hardware design flows for custom accelerators, emphasizing High-Level Synthesis (HLS) as a key enabler for rapid and reproducible hardware development. Participants will learn how modern open-source tools such as Bambu, SODA-OPT, and OpenROAD can be combined into a complete design flow from high-level (C/C++ or Python) kernels down to ASIC implementation. The session will demonstrate how open-source tools now support full circuit design flows at mature technology nodes, suitable for a wide range of application domains.

Through live demonstrations and practical guidance, attendees will gain hands-on experience with an end-to-end open-source design flow for accelerator development. Beyond practical demonstrations, the tutorial will provide an overview of how open-source methodologies are transforming education, training, and research in hardware design. The session will highlight ongoing initiatives and funded projects that strengthen the open hardware ecosystem, foster collaboration, and define long-term priorities for its growth. The tutorial will conclude with a brief overview of current and future research directions, inspiring participants to explore and contribute to the next generation of open-source EDA tools.

 

Reading list

✔    Papers:

o   Zhang, J.J., Agostini, N.B., Song, S., Tan, C., Limaye, A., Amatya, V., Manzano, J., Minutoli, M., Castellana, V.G., Tumeo, A. and Wei, G.Y., 2021, July. Towards Automatic and Agile AI/ML Accelerator Design with End-to-End Synthesis. In 2021 IEEE 32nd International Conference on Application-specific Systems, Architectures and Processors (ASAP) (pp. 218-225).

o   Ferrandi, F., Castellana, V.G., Curzel, S., Fezzardi, P., Fiorito, M., Lattuada, M., Minutoli, M., Pilato, C. and Tumeo, A., 2021, December. Bambu: an Open-Source Research Framework for the High-Level Synthesis of Complex Applications. In 2021 58th ACM/IEEE Design Automation Conference (DAC) (pp. 1327-1330).

o   Minutoli, M., Castellana, V.G., Saporetti, N., Devecchi, S., Lattuada, M., Fezzardi, P., Tumeo, A. and Ferrandi, F., 2021. Svelto: High-level synthesis of multi-threaded accelerators for graph analytics. IEEE Transactions on Computers.

o   Agostini, N.B., Curzel, S., Kaeli, D., and Tumeo, A., 2022, May, SODA-OPT an MLIR based flow for co-design and high-level synthesis. In 2022 Proceedings of the 19th ACM International Conference on Computing Frontiers (CF) (pp. 201–202).

o   Agostini, N.B., Curzel, S., Amatya, V., Tan, C., Minutoli, M., Castellana, V.G., Manzano, J., Kaeli, D., and Tumeo, A. 2022. An MLIR-based Compiler Flow for System-Level Design and Hardware Acceleration. In Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (ICCAD '22), Article 6, (pp. 1-9)

o   Agostini, N.B., Curzel, S., Zhang, J.J., Limaye, A., Tan, C., Amatya, V., Minutoli, M., Castellana, V.G., Manzano, J., Brooks, D. and Wei, G.Y., 2022, June. Bridging Python to Silicon: The SODA Toolchain. In IEEE Micro, 42(5), (pp. 78–88). BEST PAPER FOR 2022

o   Serena Curzel, Nicolas Bohm Agostini, Vito Giovanni Castellana, Marco Minutoli, Ankur Limaye, Joseph B. Manzano, Jeff Zhang, David Brooks, Gu-Yeon Wei, Fabrizio Ferrandi, Antonino Tumeo: End-to-End Synthesis of Dynamically Controlled Machine Learning Accelerators. IEEE Trans. Computers 71(12): 3074-3087 (2022)

o   Agostini, N.B., Haris, J., Gibson, P., Jayaweera, M., Rubin, N., Tumeo, A., Abellán, J.L., Cano, J., Kaeli, D.R., 2024, March, AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators. In proceedings of CGO 2024 (pp. 143-157)

✔    Additional material:

o   Other Bambu publications are listed on https://panda.dei.polimi.it/?page_id=177

o   Code repositories: https://github.com/ferrandi/PandA-bambu and https://github.com/pnnl/soda-opt

Tutorial setup

✔    Have a computer with access to internet and a valid Google account for the bambu part and access to a x86 machine with docker installed and 22GB free for the soda-opt part.

o   We will use two Jupiter Notebooks hosted on Google COLAB

▪       Notebook and material for Bambu: https://github.com/ferrandi/PandA-bambu/tree/dev/panda/documentation/bambu101

o   We will download a docker image with all tools installed: SODA docker image

▪       Notebook and material for SODA-OPT: https://github.com/pnnl/soda-benchmarks/tree/main/tutorials