Michael Muller, Anna Kantosalo, et al.
CHI 2024
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.
Michael Muller, Anna Kantosalo, et al.
CHI 2024
Hironori Takeuchi, Tetsuya Nasukawa, et al.
Transactions of the Japanese Society for Artificial Intelligence
Benjamin N. Grosof
AAAI-SS 1993
Zhikun Yuen, Paula Branco, et al.
DSAA 2023