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Thu 13 Oct 2022 10:00 - 10:20 at Banquet B - Technical Session 21 - SE for AI II Chair(s): Andrea Stocco

Today, an increasing number of Adaptive Deep Neural Networks (AdNNs) are being used to make decisions on resource-constrained embedded devices. We observe that, similar to traditional software, redundant computations exist in AdNNs, resulting in considerable performance degradation. The performance degradation in AdNNs is dependent on the input workloads, and is referred to as input-dependent performance bottlenecks (IDPBs). To ensure an AdNN satisfies the performance requirements of real-time applications, it is essential to conduct performance testing to detect IDPBs in the AdNN. Existing neural network testing methods are primarily concerned with correctness testing, which does not involve performance testing. To fill this gap, we propose DeepPerform, a scalable approach to generate test samples to detect the IDPB of AdNNs. We first demonstrate how the problem of generating performance test samples detecting IDPBs can be formulated as an optimization problem. Following that, we demonstrate how tool efficiently handles the optimization problem by learning and estimating the distribution of AdNNs’ computational consumption. We evaluate DeepPerform on three widely used datasets against five popular AdNN models. The results show that DeepPerform generates test samples that cause more severe performance degradation (FLOPs: increase up to 552%). Furthermore, DeepPerform is substantially more efficient than the baseline methods in terms of generating test inputs (runtime overhead: only 6–10 milliseconds).

Thu 13 Oct

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10:00 - 12:00
Technical Session 21 - SE for AI IIResearch Papers / Late Breaking Results / NIER Track / Journal-first Papers at Banquet B
Chair(s): Andrea Stocco Università della Svizzera italiana (USI)
10:00
20m
Research paper
DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks
Research Papers
Simin Chen University of Texas at Dallas, USA, Mirazul Haque UT Dallas, Cong Liu University of Texas at Dallas, USA, Wei Yang University of Texas at Dallas
10:20
10m
Paper
Prototyping Deep Learning Applications with Non-Experts: An Assistant Proposition
Late Breaking Results
Gustavo Rodrigues dos Reis, Adrian Mos NAVER LABS Europe, Cyril Labbé LIG - UGA, Mario Cortes Cornax LIG - UGA
10:30
20m
Research paper
Boosting the Revealing of Detected Violations in Deep Learning Testing: A Diversity-Guided MethodVirtualACM SIGSOFT Distinguished Paper Award
Research Papers
Xiaoyuan Xie School of Computer Science, Wuhan University, China, Pengbo Yin School of Computer Science, Wuhan University, Songqiang Chen School of Computer Science, Wuhan University
10:50
20m
Paper
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection ApproachVirtual
Journal-first Papers
Amin Nikanjam École Polytechnique de Montréal, Mohammad Mehdi Morovati École Polytechnique de Montréal, Foutse Khomh Polytechnique Montréal, Houssem Ben Braiek École Polytechnique de Montréal
Link to publication DOI Authorizer link
11:10
20m
Research paper
Towards Understanding the Faults of JavaScript-Based Deep Learning SystemsVirtual
Research Papers
Lili Quan Tianjin University, Qianyu Guo College of Intelligence and Computing, Tianjin University, Xiaofei Xie Singapore Management University, Singapore, Sen Chen Tianjin University, Xiaohong Li TianJin University, Yang Liu Nanyang Technological University
11:30
10m
Vision and Emerging Results
An Empirical Study on Numerical Bugs in Deep Learning ProgramsVirtual
NIER Track
Gan Wang , Zan Wang Tianjin University, China, Junjie Chen Tianjin University, Xiang Chen Nantong University, Ming Yan College of Intelligence and Computing, Tianjin University
11:40
20m
Research paper
Toward Improving the Robustness of Deep Learning Models via Model TransformationVirtual
Research Papers
Yingyi Zhang College of Intelligence and Computing, Tianjin University, Zan Wang Tianjin University, China, Jiajun Jiang Tianjin University, Hanmo You College of Intelligence and Computing, Tianjin University, Junjie Chen Tianjin University