50 citations · 59 across the 3 of their papers we have counts for
3 papers
Correctly Rounded Functions For Vector Applications: A Performance Study
Cristina Anderson, Marius Cornea, Andrey Stepin +1
Following recent interest in correctly rounded math library functions (as currently recommended by the IEEE 754 standard), we have designed several SIMD algorithms for one-input si…
Microscaling Data Formats for Deep Learning
Bita Darvish Rouhani, Ritchie Zhao, Ankit More +30
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that…
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…