188金宝慱亚洲体育

188金宝慱亚洲体育 > 学术信息 > 正文

学术信息

微软亚洲研究院曹婷研究员学术报告

来源: 点击: 时间:2021年04月01日 11:44

报告人:微软亚洲研究院 曹婷研究员


报告地点:校本部升华后楼409


报告时间:202142日(周五)上午10


报告题目:EmpowerEfficientDNNInferenceonEdgeDevices


个人简介:

Ting Cao is now a Senior Researcher in HEX Group of MSRA. Her research interests include big data and deep learning systems, Hardware/Software co-design, high-level language implementation, and energy efficient hardware design.


She received her PhD from Research School of Computer Science, the Australian National University. Before joining MSRA, she worked in the Compiler and Computing Language Lab in 2012 Labs, Huawei Technologies. She has publications in top-tier conferences, such as PLDI, ISCA, ASPLOS, MobiCom, and MobiSys. She has also served as PC or EPC in a range of conferences such as PLDI, OOPSLA, VEE, and ISMM.

报告摘要:

The boom of edge AI applications has spawned a great many innovations for efficient neural network (NN) algorithms and inference frameworks. Inference on edge devices has specific challenges compared to servers, such as diverse edge AI platforms, weak hardware resources and contradictory requirements for energy and performance. However, existing works do not well consider these challenges, which leads to suboptimal performance.


This talk will introduce our recent progress which aims to empower efficient edge inference by incorporating edge hardware features into the process of NN algorithm and framework design. To enable efficient NN design for diverse edge AI platforms, as a first stage, we propose to use a characterization method to quantify HW behaviour of NN design space. The results are used as heuristics for NN design. To apply our heuristics in a real NN design work as a case study, our designed models have higher accuracy and efficiency on every experimental platform with much lower design cost. Furthermore, to improve edge CPU and GPU utilization for inference, we propose several novel techniques in edge inference frameworks, including cost-model directed block partition, asymmetry aware scheduling, rule-based work group size selection, etc. The inference performance is improved by 87% on the CPU and 15% on the GPU on average compared to the state-of-the-art methods.


  • 常用系统:
  • 188金宝慱亚洲体育
  • |
  • 188金宝慱亚洲体育行政管理系统
  • |
  • 会议预订系统
  • |

联系方式:0731-88879336 地址:湖南省长沙市岳麓区188金宝慱亚洲体育计算机楼

Copyright ? 2017-2019 188金宝慱亚洲体育 All Rights Reserved.

188金宝慱亚洲体育-188金宝搏下载手机版
Baidu
sogou