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Bin Ren
Researcher at Xiamen University
Publications - 528
Citations - 30728
Bin Ren is an academic researcher from Xiamen University. The author has contributed to research in topics: Raman spectroscopy & Surface-enhanced Raman spectroscopy. The author has an hindex of 73, co-authored 470 publications receiving 23452 citations. Previous affiliations of Bin Ren include Pacific Northwest National Laboratory & Max Planck Society.
Papers
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Journal ArticleDOI
In situ photoluminescence studies of silicon surfaces during photoelectrochemical etching processes
TL;DR: The photoluminescence (PL) from silicon surfaces during photoelectrochemical etching processes was monitored in situ by using a confocal microprobe spectrometer.
Proceedings ArticleDOI
Brief Industry Paper: Towards Real-Time 3D Object Detection for Autonomous Vehicles with Pruning Search
Pu Zhao,Wei Niu,Geng Yuan,Yuxuan Cai,Hsin-Hsuan Sung,Shaoshan Liu,Sijia Liu,Xipeng Shen,Bin Ren,Yanzhi Wang,Xue Lin +10 more
TL;DR: In this paper, a compiler-aware pruning search framework is proposed to achieve real-time inference of 3D object detection on the resource-limited mobile devices, where a generator is applied to sample better pruning proposals in the search space based on current proposals with their performance, and an evaluator is adopted to evaluate the sampled pruning proposal performance.
Proceedings ArticleDOI
BLCR: Towards Real-time DNN Execution with Block-based Reweighted Pruning
Xiaolong Ma,Geng Yuan,Zhengang Li,Yifan Gong,Tianyun Zhang,Wei Niu,Zheng Zhan,Pu Zhao,Ning Liu,Jian Tang,Xue Lin,Bin Ren,Yanzhi Wang +12 more
TL;DR: BLCR is proposed, a novel block-based pruning framework that comprises a general and flexible structured pruning scheme that enjoys higher flexibility while exploiting full on-device parallelism, as well as a powerful and efficient reweighted regularization method to achieve the proposed sparsity scheme.
Proceedings ArticleDOI
A combined SERS and MCBJ study on molecular junctions on silicon chips
Jing-Hua Tian,Bo Liu,Shan Jin,Ke Dai,Zhao-Bin Chen,Xiulan Li,Hui-Xian Ke,Sun-Tao Wu,Yang Yang,Bin Ren,Bing-Wei Mao,Nongjian Tao,Zhong-Qun Tian +12 more
TL;DR: In this article, a combined surface-enhanced raman spectroscopy (SERS) and mechanically controllable break junction (MCBJ) method was developed to detect and characterize molecular junctions formed by two electrochemically nanofabricated electrodes on silicon chips.
Posted Content
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge.
Geng Yuan,Xiaolong Ma,Wei Niu,Zhengang Li,Zhenglun Kong,Ning Liu,Yifan Gong,Zheng Zhan,Chaoyang He,Qing Jin,Siyue Wang,Minghai Qin,Bin Ren,Yanzhi Wang,Sijia Liu,Xue Lin +15 more
TL;DR: In this article, a novel Memory-Economic Sparse Training (MEST) framework is proposed for accurate and fast execution on edge devices. And the authors explore the impact of model sparsity, sparsity schemes, and sparse training algorithms on the number of removable training examples.