Special Sessions - Hardware-Software Co-Design for Machine Learning Systems Made Open-Source
Mehdi Tahoori, Vincent Meyers, Mahboobe Sadeghipour Roodsari, Huashuangyang Xu, Juergen Becker, Tanja Harbaum, Felix Frombach, Julian Hoefer, Georgios Sotiropoulos, Jorg Henkel, Zeynep Demirdag, Heba Khdr, Hassan Nassar, Ulf Schlichtmann, Johannes Geier, Philipp van Kempen, Georg Sigl, Stefan Koegler, Matthias Probst, Jurgen Teich · 2025
Chip technologies are crucial for the digital transformation of industry and society. Machine Learning (ML) and Artificial Intelligence (AI) are increasingly shaping both daily life and industrial applications, with AI hardware playing a vital role in enabling efficient and scalable ML deployment. However, significant challenges remain in bridging the gap between ML algorithm development and hardware implementation, particularly for edge ML applications where efficiency, power constraints, and adaptability are critical. In such resource-constrained environments, hardware-software co-design becomes essential to achieve the necessary trade-offs between performance, energy efficiency, and system responsiveness. One of the key bottlenecks in ML hardware development is the lack of seamless integration between ML toolchains and electronic design automation (EDA) tools for hardware synthesis and mapping. Current solutions often require extensive manual optimization and costly proprietary software, limiting accessibility and innovation. Open-source tools can play a transformative role in democratizing ML hardware design, fostering collaboration, and addressing the growing shortage of skilled professionals. This paper covers key aspects of hardware-software co-design for ML systems, such as ML algorithms, hardware design, compiler technologies and system security, with a focus on open-source solutions. We highlight the critical need for open-source toolchains that connect ML model development with hardware synthesis and optimization and present solutions for custom hardware, as well as FPGA accelerators.