Katsuyuki Sakuma, Mukta Farooq, et al.
ECTC 2021
Many recent breakthroughs in AI and science were only possible due to the availability of ever-more powerful computing systems. The architecture of the systems used to run classic HPC applications at exascale and the systems training trillion-parameter AI models is converging, but the explosion of the amounts of data, the power & energy limitations, the slow-down of Moore’s law, and the operational complexity (including security, data management, development environment, etc) need to be addressed. This demands a fresh look at system architecture, where data takes the center stage and defines key architectural elements in a data-driven approach. As a result, we propose a system architecture that combines a data and service oriented core with high-performance, energy efficient, domain-specific compute HW, which gets integrated, e.g., as chiplets into the overall system. The increased complexity of these system also requires a co-design approach, where the programming environment (languages, compilers, run-time components) is developed and adopted together with the new technologies.
Katsuyuki Sakuma, Mukta Farooq, et al.
ECTC 2021
Olivier Maher, N. Harnack, et al.
DRC 2023
Divya Taneja, Jonathan Grenier, et al.
ECTC 2024
Max Bloomfield, Amogh Wasti, et al.
ITherm 2025