Chen-chia Chang, Wan-hsuan Lin, et al.
ICML 2025
Power awareness is fast becoming immensely important in computing, ranging from the traditional high-performance computing applications to the new generation of data centric workloads. In this work, we describe our efforts towards a powerefficient computing paradigm that combines lowand high-precision arithmetic.We showcase our ideas for the widely used kernel of solving systems of linear equations that finds numerous applications in scientific and engineering disciplines as well as in large-scale data analytics, statistics and machine learning. Towards this goal, we developed tools for the seamless power profiling of applications at a finegrain level. In addition, we verify here previous work on post-FLOPS/W metrics and show that these can shed much more light in the power/energy profile of important applications. © 2014 The Author(s) Published by the Royal Society. All rights reserved.
Chen-chia Chang, Wan-hsuan Lin, et al.
ICML 2025
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks
Saeel Sandeep Nachane, Ojas Gramopadhye, et al.
EMNLP 2024
Freddy Lécué, Jeff Z. Pan
IJCAI 2013