124 citations · 311 across the 19 of their papers we have counts for
3 papers · 1 filter
FP8 Formats for Deep Learning
Paulius Micikevicius, Dusan Stosic, Neil Burgess +12
FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors. In this paper we propose an 8-bit floating poi…
FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems
Rui Ma, Evangelos Georganas, Alexander Heinecke +2
Rapid advances in artificial intelligence (AI) technology have led to significant accuracy improvements in a myriad of application domains at the cost of larger and more compute-in…
Next-Generation Local Time Stepping for the ADER-DG Finite Element Method
Alexander Breuer, Alexander Heinecke
High-frequency ground motion simulations pose a grand challenge in computational seismology. Two main factors drive this challenge. First, to account for higher frequencies, we hav…