ATHENA: Enabling Codesign for Next-Generation AI/ML Architectures

There is a growing market for technologies ded-icated to accelerating Artificial Intelligence (AI) workloads. Many of these emerging architectures promise to provide savings in energy efficiency, area, and latency when compared to traditional CPUs for these types of applications. In particular, neuromorphic analog and digital technologies provide both low-power and configurable acceleration of challenging artificial intelligence (AI) algorithms. If designed into a heterogeneous system with other accelerators and conventional compute nodes, these technologies have the potential to augment the capabilities of traditional High Performance Computing (HPC) platforms. We present a codesign ecosystem that leverages an analytical tool, ATHENA, to accelerate design space exploration and evaluation of novel architectures.

@inproceedings{plagge2022athena,
  author    = {Mark Plagge and Ben Feinberg and John McFarland and Fred Rothganger and Sapan Agarwal and Amro Awad and Clayton Hughes and Suma Cardwell},
  title     = {{ATHENA: Enabling Codesign for Next-Generation AI/ML Architectures}},
  booktitle = {IEEE International Conference on Rebooting Computing (ICRC)},
  year      = {2022},
  month     = {dec},
  address   = {Virtual},
  doi       = {10.1109/ICRC57508.2022.00016}
}