Thursday, 8 February 2018
ACM ReQuEST: 1st open and reproducible tournament to co-design Pareto-efficient deep learning (speed, accuracy, energy, size, costs)
The first Reproducible Quality-Efficient Systems Tournament (ReQuEST) will debut at ASPLOS’18 ( ACM conference on Architectural Support for Programming Languages and Operating Systems, which is the premier forum for multidisciplinary systems research spanning computer architecture and hardware, programming languages and compilers, operating systems and networking).
Organized by a consortium of leading universities (Washington, Cornell, Toronto, Cambridge, EPFL) and the cTuning foundation, ReQuEST aims to provide a open-source tournament framework, a common experimental methodology and an open repository for continuous evaluation and multi-objective optimization of the quality vs. efficiency Pareto optimality of a wide range of real-world applications, models and libraries across the whole software/hardware stack.
ReQuEST will use the established artifact evaluation methodology together with the Collective Knowledge framework validated at leading ACM/IEEE conferences to reproduce results, display them on a live dashboard and share artifacts with the community. Distinguished entries will be presented at the associated workshop and published in the ACM Digital Library. To win, the results of an entry do not necessarily have to lie on the Pareto frontier, as an entry can be also praised for its originality, reproducibility, adaptability, scalability, portability, ease of use, etc.
The first ReQuEST competition will focus on deep learning for image recognition with an ambitious long-term goal to build a public repository of portable and customizable “plug&play” AI/ML algorithms optimized across diverse data sets, models and platforms from IoT to supercomputers (see live demo). Future competitions will consider other emerging workloads, as suggested by our Industrial Advisory Board.
For more information, please visit http://cKnowledge.org/request
Posted by Grigori Fursin at 01:17