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Evaluating Efficient Performance Estimators of Neural Architectures
Xuefei Ning · Changcheng Tang · Wenshuo Li · Zixuan Zhou · Shuang Liang · Huazhong Yang · Yu Wang

Wed Dec 08 04:30 PM -- 06:00 PM (PST) @ Virtual #None

Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS). To reduce the architecture training costs in NAS, one-shot estimators (OSEs) amortize the architecture training costs by sharing the parameters of one "supernet" between all architectures. Recently, zero-shot estimators (ZSEs) that involve no training are proposed to further reduce the architecture evaluation cost. Despite the high efficiency of these estimators, the quality of such estimations has not been thoroughly studied. In this paper, we conduct an extensive and organized assessment of OSEs and ZSEs on five NAS benchmarks: NAS-Bench-101/201/301, and NDS ResNet/ResNeXt-A. Specifically, we employ a set of NAS-oriented criteria to study the behavior of OSEs and ZSEs and reveal that they have certain biases and variances. After analyzing how and why the OSE estimations are unsatisfying, we explore how to mitigate the correlation gap of OSEs from several perspectives. Through our analysis, we give out suggestions for future application and development of efficient architecture performance estimators. Furthermore, the analysis framework proposed in our work could be utilized in future research to give a more comprehensive understanding of newly designed architecture performance estimators.

Author Information

Xuefei Ning (Tsinghua University)
Changcheng Tang (Beijing Novauto Co. Ltd)
Wenshuo Li (Tsinghua University)
Zixuan Zhou (Tsinghua University, Tsinghua University)
Shuang Liang
Huazhong Yang
Yu Wang (Tsinghua University)

Yu Wang received his B.S. degree in 2002 and Ph.D. degree (with honor) in 2007 from Tsinghua University, Beijing. He is currently a Tenured Associate Professor with the Department of Electronic Engineering, Tsinghua University. His research interests include brain inspired computing, application specific hardware computing, parallel circuit analysis, and power/reliability aware system design methodology. Dr. Wang has authored and coauthored over 150 papers in refereed journals and conferences. He has received Best Paper Award in FPGA 2017, ISVLSI 2012, and Best Poster Award in HEART 2012 with 8 Best Paper Nominations. He is a recipient of IBM X10 Faculty Award in 2010. He served as TPC chair for ICFPT 2011 and Finance Chair of ISLPED 2012-2016, and served as program committee member for leading conferences in these areas, including top EDA conferences such as DAC, DATE, ICCAD, ASP-DAC, and top FPGA conferences such as FPGA and FPT. Currently he serves as Co-EIC for SIGDA E-Newsletter, Associate Editor for IEEE Transactions on CAD and Journal of Circuits, Systems, and Computers. He also serves as guest editor for Integration, the VLSI Journal and IEEE Transactions on Multi-Scale Computing Systems. He is a recipient of NSFC Excellent Young Scholar´╝îand is now serving as ACM distinguished speaker. He is an IEEE/ACM senior member.

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