148 citations · 858 across the 24 of their papers we have counts for
24 papers
HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware
Nicolas PD Sawaya, Daniel Marti-Dafcik, Yang Ho +13
In order to characterize and benchmark computational hardware, software, and algorithms, it is essential to have many problem instances on-hand. This is no less true for quantum co…
AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks
Abhisek Kundu, Naveen K. Mellempudi, Dharma Teja Vooturi +2
Sparse training is emerging as a promising avenue for reducing the computational cost of training neural networks. Several recent studies have proposed pruning methods using learna…
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…
Systolic Computing on GPUs for Productive Performance
Hongbo Rong, Xiaochen Hao, Yun Liang +3
We propose a language and compiler to productively build high-performance {\it software systolic arrays} that run on GPUs. Based on a rigorous mathematical foundation (uniform recu…
MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure
Fangke Ye, Shengtian Zhou, Anand Venkat +10
Code semantics similarity can be used for many tasks such as code recommendation, automated software defect correction, and clone detection. Yet, the accuracy of such systems has n…
Context-Aware Parse Trees
Fangke Ye, Shengtian Zhou, Anand Venkat +8
The simplified parse tree (SPT) presented in Aroma, a state-of-the-art code recommendation system, is a tree-structured representation used to infer code semantics by capturing pro…