Special Sessions - Hardware-Software Co-Design for Machine Learning Systems Made Open-Source

Tahoori, Mehdi and Meyers, Vincent and Sadeghipour Roodsari, Mahboobe and Xu, Huashuangyang and Becker, Juergen and Harbaum, Tanja and Frombach, Felix and Hoefer, Julian and Sotiropoulos, Georgios and Henkel, Jorg and Demirdag, Zeynep and Khdr, Heba and Nassar, Hassan and Schlichtmann, Ulf and Geier, Johannes and van Kempen, Philipp and Sigl, Georg and Koegler, Stefan and Probst, Matthias and Teich, Jurgen and Hannig, Frank and Sabih, Muhammad and Sesli, Batuhan and Wehn, Norbert and Steiner, Lukas and Kunz, Wolfgang and Shelkamy Ali, Mohamed     «

Tahoori, Mehdi and Meyers, Vincent and Sadeghipour Roodsari, Mahboobe and Xu, Huashuangyang and Becker, Juergen and Harbaum, Tanja and Frombach, Felix and Hoefer, Julian and Sotiropoulos, Georgios and Henkel, Jorg and Demirdag, Zeynep and Khdr, Heba and Nassar, Hassan and Schlichtmann, Ulf and Geier, Johannes and van Kempen, Philipp and Sigl, Georg and Koegler, Stefan and Probst, Matthias and Teich, Jurgen and Hannig, Frank and Sabih, Muhammad and Sesli, Batuhan and Wehn, Norbert and Steiner, Lu...     »

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.     «

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 a...     »