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Tianwei Xing
Researcher at University of California, Los Angeles
Publications - 17
Citations - 592
Tianwei Xing is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 7, co-authored 15 publications receiving 433 citations. Previous affiliations of Tianwei Xing include University of California.
Papers
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Proceedings ArticleDOI
Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs
Ritchie Zhao,Weinan Song,Wentao Zhang,Tianwei Xing,Jeng-Hau Lin,Mani Srivastava,Rajesh Gupta,Zhiru Zhang +7 more
TL;DR: The design of a BNN accelerator is presented that is synthesized from C++ to FPGA-targeted Verilog and outperforms existing FPGAs-based CNN accelerators in GOPS as well as energy and resource efficiency.
Journal ArticleDOI
Personalized Course Sequence Recommendations
TL;DR: A forward-search backward-induction algorithm is developed that can optimally select course sequences to decrease the time required for a student to graduate and optimally recommend a course sequence that reduces the time to graduate while also increasing the overall GPA of the student.
Proceedings ArticleDOI
Enabling Edge Devices that Learn from Each Other: Cross Modal Training for Activity Recognition
TL;DR: RecycleML uses cross modal transfer to accelerate the learning of edge devices across different sensing modalities and reduces the amount of required labeled data by at least 90% and speeds up the training process by up to 50 times in comparison to training the edge device from scratch.
Proceedings ArticleDOI
Why the Failure? How Adversarial Examples Can Provide Insights for Interpretable Machine Learning
Richard Tomsett,Amy Widdicombe,Tianwei Xing,Supriyo Chakraborty,Simon Julier,Prudhvi Gurram,Raghuveer M. Rao,Mani Srivastava +7 more
TL;DR: This paper argues that these two issues are conceptually linked, and insights in one can provide insights in the other, and illustrates these ideas with relevant examples from the literature and the authors' own experiments.
Proceedings ArticleDOI
Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration
TL;DR: This paper proposes BCNN with Separable Filters (BCNNw/SF), which applies Singular Value Decomposition (SVD) on BCNN kernels to further reduce computational and storage complexity.