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学术报告——Towards Pervasive and User Satisfactory Deep Learning in Big Data Era
时间: 2017-09-12 10:57

 

报告题目:Towards Pervasive and User Satisfactory Deep Learning in Big Data Era

报告人:Tao LiProfessorDepartment of Electrical and Computer EngineeringUniversity of Florida

报告时间:201791815:00

报告地点:学院报告厅(望江校区基础教学大楼B302

 

报告内容:

Accelerating Convolutional Neural Networks (CNNs) on GPUs usually involves two stages: training and inference. Traditionally, this two-stage process is deployed on high-end GPU-equipped servers. Driven by the increase in compute power of desktop and mobile GPUs, there is growing interest in performing inference on various kinds of platforms. In contrast to the requirements of high throughput and accuracy during the training stage, end-users will face diverse requirements related to inference tasks. To address this emerging trend and new requirements, we propose Pervasive CNN (P-CNN), a user satisfaction-aware CNN inference framework. P-CNN is composed of two phases: cross-platform offline compilation and run-time management. Based on users' requirements, offline compilation generates the optimal kernel using architecture- independent techniques, such as adaptive batch size selection and coordinated fine-tuning. The runtime management phase consists of accuracy tuning, execution, and calibration. First, accuracy tuning dynamically identifies the fastest kernels with acceptable accuracy. Next, the run-time kernel scheduler partitions the optimal computing resource for each layer and schedules the GPU thread blocks. If its accuracy is not acceptable to the end-user, the calibration stage selects a slower but more precise kernel to improve the accuracy. Finally, we design a user satisfaction metric for CNNs to evaluate our Pervasive deign. Our evaluation results show P-CNN can provide the best user satisfaction for different inference tasks.

 

报告人简介:

李涛博士是美国佛罗里达大学工程学院电子与计算机工程系教授,智能计算机体系结构设计实验室主任。2004年于美国德克萨斯大学奥斯汀分校获得计算机工程博士学位。2013年获Yahoo!重大研究计划挑战奖。2009年获美国国家科学基金会杰出青年教授奖(NSF CAREER Award)。2008年,2007年,2006年均获 IBM 学院奖(IBM Faculty Award)。2008年获得美国微软研究院安全及可扩展多核计算机奖。2006年获得微软研究院可信计算课程研究奖。2012, 2014两度获佛罗里达大学工程学院年度最佳博士生论文导师奖。在高性能计算机体系结构、高效/可靠/低功耗微处理器及存储系统、面向云计算和大数据数据中心、虚拟化、并行与分布式计算、新型及可重构计算架构、面向特定应用计算架构、多核容错处理器、片上互连网络、面向多众核的可扩展体系架构、新型前瞻技术及应用对硬件和操作系统的影响、嵌入式与片上系统、以及计算机系统性能评估等诸多领域取得了多项开创性成果。在著名的国际期刊(大部分为 IEEE/ACM 期刊)和计算机体系结构类一级国际会议 ISCA MICROHPCAALPLOS SIGMETRICS PACT DSN发表论文120余篇,同时还获得10多项美国及中国发明专利。其中9篇论文被HPCA’17ICCD’16ICPP’15CGO’14HPCA’11DSN’11MICRO’08IISWC’07 MASCOTS’06会议程序委员会推荐参选最佳论文奖。获ICCD’16HPCA’11最佳论文以及IEEE Computer Architecture Letters 2015度最佳论文。

 

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