SODA Synthesizer: Accelerating Artificial Intelligence Applications

with an End-to-End Silicon Compiler

CGO 2026 – Saturday, January 31, Balmoral

Nicolas Bohm Agostini (PNNL), Michele Fiorito (Politecnico di Milano), Giovanni Gozzi (Politecnico di Milano), Serena Curzel (Politecnico di Milano), Ankur Limaye (PNNL), Vito Giovanni Castellana (PNNL),

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

Abstract

Artificial intelligence applications (machine learning, graph analytics) are among the main drivers for the renewed interests in designing domain specific accelerators, both for reconfigurable devices (e.g., field programmable gate arrays - FPGAs) and application-specific integrated circuits (ASICs). The constant evolution of the algorithms and models does not allow the conventional hardware design cycle to keep up. New agile hardware design methods and tools that could convert high-level descriptions of algorithms in their hardware implementation and allow to explore them along several contrasting design metrics with minimal human interventions are needed. This tutorial will discuss methodologies, trends, advantages, benefits, and gaps that still needs to be closed for agile hardware design tools based on compiler and high-level synthesis (HLS) technologies. The tutorial will prvide a hands-on ands-on experience of the SOftware Defined Accelerators (SODA) Synthesizer, an open-source compiler-based toolchain composed of SODA-OPT, a front-end and optimizer that interfaces with productive programming data science frame-works in Python based on the MLIR framework, and Bambu, the most advanced open-source HLS tool available, able to generate optimized accelerators for data-intensive kernels.

 

 

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)

o   Giovanni Gozzi, Michele Fiorito, Serena Curzel, Claudio Barone, Vito Giovanni Castellana, Marco Minutoli, Antonino Tumeo, Fabrizio Ferrandi: SPARTA: High-Level Synthesis of Parallel Multi-Threaded Accelerators. ACM Trans. Reconfigurable Technol. Syst. 18(1): 9:1-9:30 (2025)

✔    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