Invited Talk Abstract: Introducing ReQuEST: An Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-03-01 DOI:10.1109/emc2.2018.00008

G. Fursin

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引用次数: 1

Abstract

Co-designing efficient machine learning based systems across the whole application/hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming. Researchers often struggle to evaluate and compare different published works across rapidly evolving software frameworks, heterogeneous hardware platforms, compilers, libraries, algorithms, data sets, models, and environments. I will present our community effort to develop an open co-design tournament platform with an online public scoreboard based on Collective Knowledge workflow framework (CK). It gradually incorporates best research practices while providing a common way for multidisciplinary researchers to optimize and compare the quality vs. efficiency Pareto optimality of various workloads on diverse and complete hardware/software systems. All the winning solutions will be made available to the community as portable and customizable "plug&play" components with a common API to accelerate research and innovation! I will then discuss how our open competition and collaboration can help to achieve energy efficiency for cognitive workloads based on energy-efficient submissions from the 1st ReQuEST tournament co-located with ASPLOS'18. Further details: http://cKnowledge.org/request

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摘要:介绍ReQuEST:一个可复制和高质量系统的开放平台- ml锦标赛

在整个应用程序/硬件/软件堆栈中共同设计高效的基于机器学习的系统，以权衡速度、准确性、能源和成本，这变得非常复杂和耗时。研究人员经常努力评估和比较快速发展的软件框架、异构硬件平台、编译器、库、算法、数据集、模型和环境中不同的已发表作品。我将介绍我们的社区努力开发一个开放的共同设计比赛平台，该平台基于集体知识工作流框架(CK)，具有在线公共计分板。它逐渐融合了最佳研究实践，同时为多学科研究人员提供了一种通用的方法来优化和比较不同和完整的硬件/软件系统上各种工作负载的质量与效率的帕累托最优性。所有获胜的解决方案都将作为可移植和可定制的“即插即用”组件提供给社区，并使用通用API来加速研究和创新!然后，我将讨论我们的开放竞争和协作如何帮助实现基于与ASPLOS'18共同举办的第一届请求锦标赛的节能提交的认知工作负载的能源效率。更多详细信息:http://cKnowledge.org/request

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2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2)

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