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Tianqi Tang
Researcher at Tsinghua University
Publications - 30
Citations - 2617
Tianqi Tang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Convolutional neural network & Neuromorphic engineering. The author has an hindex of 18, co-authored 28 publications receiving 1901 citations. Previous affiliations of Tianqi Tang include University of California, Santa Barbara.
Papers
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Proceedings ArticleDOI
Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
Jiantao Qiu,Jie Wang,Song Yao,Kaiyuan Guo,Boxun Li,Erjin Zhou,Jincheng Yu,Tianqi Tang,Ningyi Xu,Sen Song,Yu Wang,Huazhong Yang +11 more
TL;DR: This paper presents an in-depth analysis of state-of-the-art CNN models and shows that Convolutional layers are computational-centric and Fully-Connected layers are memory-centric, and proposes a CNN accelerator design on embedded FPGA for Image-Net large-scale image classification.
Journal ArticleDOI
A Survey of Accelerator Architectures for Deep Neural Networks
TL;DR: Various architectures that support DNN executions in terms of computing units, dataflow optimization, targeted network topologies, architectures on emerging technologies, and accelerators for emerging applications are discussed.
Proceedings ArticleDOI
Binary convolutional neural network on RRAM
TL;DR: An RRAM crossbar-based accelerator is proposed for BCNN forward process and shows much smaller accuracy loss than multi-bit CNNs for LeNet on MNIST when considering device variation.
Journal ArticleDOI
Technological Exploration of RRAM Crossbar Array for Matrix-Vector Multiplication
Lixue Xia,Peng Gu,Boxun Li,Tianqi Tang,Xiling Yin,Wenqin Huangfu,Shimeng Yu,Yu Cao,Yu Wang,Huazhong Yang +9 more
TL;DR: This paper analyzes the impact of both device level and circuit level non-ideal factors, including the nonlinear current-voltage relationship of RRAM devices, the variation of device fabrication and write operation, and the interconnect resistance as well as other crossbar array parameters, and proposes a technological exploration flow for device parameter configuration.
Journal ArticleDOI
Stuck-at Fault Tolerance in RRAM Computing Systems
Lixue Xia,Wenqin Huangfu,Tianqi Tang,Xiling Yin,Krishnendu Chakrabarty,Yuan Xie,Yu Wang,Huazhong Yang +7 more
TL;DR: A mapping algorithm with inner fault tolerance is proposed to convert matrix parameters into RRAM conductances in RCS and tolerate SAFs by fully exploring the available mapping space to ensure that RCS is effective when the percentage of faulty RRAM cells is high.