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Tue 10 Nov 2020 01:03 - 01:04 at Virtual room 1 - ML In Practice

Autonomous Driving System (ADS) is one of the most promising and valuable large-scale machine learning (ML) powered systems. Hence, ADS has attracted much attention from academia and practitioners in recent years. Despite extensive study on ML models, it still lacks a comprehensive empirical study towards understanding the ML model roles, peculiar architecture, and complexity of ADS (i.e., various ML models and their relationship with non-trivial code logic). In this paper, we conduct an in-depth case study on Apollo, which is one of the state-of-the-art ADS, widely adopted by major automakers worldwide. We took the first step to reveal the integration of the underlying ML models and code logic in Apollo. In particular, we study the Apollo source code and present the underlying ML model system architecture. We present our findings on how the ML models interact with each other, and how the ML models are integrated with code logic to form a complex system. Finally, we inspect Apollo in a dynamic view and notice the heavy use of model-relevant components and the lack of adequate tests in general.
Our study reveals potential maintenance challenges of complex ML-powered systems and identifies future directions to improve the quality assurance of ADS and general ML systems.

Tue 10 Nov

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01:00 - 01:30
01:00
2m
Talk
A Comprehensive Study on Challenges in Deploying Deep Learning Based Software
Research Papers
Zhenpeng Chen Peking University, China, Yanbin Cao Peking University, China, Yuanqiang Liu Peking University, China, Haoyu Wang Beijing University of Posts and Telecommunications, Tao Xie Peking University, Xuanzhe Liu Peking University, China
DOI Pre-print
01:03
1m
Talk
A First Look at the Integration of Machine Learning Models in Complex Autonomous Driving Systems: A Case Study on Apollo
Industry Papers
pengzi Concordia University, Canada, Jinqiu Yang Concordia University, Montreal, Canada, Tse-Hsun (Peter) Chen Concordia University, Lei Ma Kyushu University
DOI
01:05
1m
Talk
Enhancing the Interoperability between Deep Learning Frameworks by Model Conversion
Industry Papers
Yu David Liu SUNY Binghamton, USA, Cheng Chen ByteDance, China, Ru Zhang Microsoft Research, Tingting Qin Microsoft Research, China, Xiang Ji Microsoft Research, China, Haoxiang Lin Microsoft Research, Mao Yang Microsoft Research
DOI
01:07
1m
Talk
Estimating GPU Memory Consumption of Deep Learning Models
Industry Papers
Yanjie Gao Microsoft Research, China, Yu David Liu SUNY Binghamton, USA, Hongyu Zhang University of Newcastle, Australia, lizhengxian Microsoft Research, China, Yonghao Zhu Microsoft Research, China, Haoxiang Lin Microsoft Research, Mao Yang Microsoft Research
DOI
01:09
1m
Talk
IntelliCode Compose: Code Generation using Transformer
Industry Papers
Alexey Svyatkovskiy Microsoft, Shao Kun Deng Microsoft Corporation, Shengyu Fu Microsoft, USA, Neel Sundaresan Microsoft Corporation
DOI Pre-print
01:11
1m
Talk
Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving
Industry Papers
Jinhan Kim KAIST, Jeongil Ju Hyundai Motor Group, South Korea, Robert Feldt Chalmers University of Technology, Sweden, Shin Yoo Korea Advanced Institute of Science and Technology
DOI Pre-print
01:13
17m
Conversations on ML In Practice
Research Papers
Sidong Feng Australian National University, Australia, Tse-Hsun (Peter) Chen Concordia University, Yanbin Cao Peking University, China, Yanjie Gao Microsoft Research, China, Zhenpeng Chen Peking University, China, M: Joshua Garcia University of California, Irvine