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Ning Liu 0007
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Publications
- 2022
- [c22]Shaoyi Huang, Ning Liu, Yueying Liang, Hongwu Peng, Hongjia Li, Dongkuan Xu, Mimi Xie, Caiwen Ding:
An Automatic and Efficient BERT Pruning for Edge AI Systems. ISQED 2022: 1-6 - [i17]Shaoyi Huang, Ning Liu, Yueying Liang, Hongwu Peng, Hongjia Li, Dongkuan Xu, Mimi Xie, Caiwen Ding:
An Automatic and Efficient BERT Pruning for Edge AI Systems. CoRR abs/2206.10461 (2022) - 2019
- [c15]Caiwen Ding, Shuo Wang, Ning Liu, Kaidi Xu, Yanzhi Wang, Yun Liang:
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs. FPGA 2019: 33-42 - [c14]Ruizhe Cai, Olivia Chen, Ao Ren, Ning Liu, Caiwen Ding, Nobuyuki Yoshikawa, Yanzhi Wang:
A Majority Logic Synthesis Framework for Adiabatic Quantum-Flux-Parametron Superconducting Circuits. ACM Great Lakes Symposium on VLSI 2019: 189-194 - [c11]Ruizhe Cai, Ao Ren, Olivia Chen, Ning Liu, Caiwen Ding, Xuehai Qian, Jie Han, Wenhui Luo, Nobuyuki Yoshikawa, Yanzhi Wang:
A stochastic-computing based deep learning framework using adiabatic quantum-flux-parametron superconducting technology. ISCA 2019: 567-578 - [c9]Hongjia Li, Sheng Lin, Ning Liu, Caiwen Ding, Yanzhi Wang:
Deep Compressed Pneumonia Detection for Low-Power Embedded Devices. LABELS/HAL-MICCAI/CuRIOUS@MICCAI 2019: 89-97 - [i9]Ruizhe Cai, Ao Ren, Olivia Chen, Ning Liu, Caiwen Ding, Xuehai Qian, Jie Han, Wenhui Luo, Nobuyuki Yoshikawa, Yanzhi Wang:
A Stochastic-Computing based Deep Learning Framework using Adiabatic Quantum-Flux-Parametron SuperconductingTechnology. CoRR abs/1907.09077 (2019) - [i8]Caiwen Ding, Shuo Wang, Ning Liu, Kaidi Xu, Yanzhi Wang, Yun Liang:
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs. CoRR abs/1909.13396 (2019) - [i7]Hongjia Li, Sheng Lin, Ning Liu, Caiwen Ding, Yanzhi Wang:
Deep Compressed Pneumonia Detection for Low-Power Embedded Devices. CoRR abs/1911.02007 (2019) - 2018
- [c8]Ruizhe Cai, Ao Ren, Ning Liu, Caiwen Ding, Luhao Wang, Xuehai Qian, Massoud Pedram, Yanzhi Wang:
VIBNN: Hardware Acceleration of Bayesian Neural Networks. ASPLOS 2018: 476-488 - [c7]Sheng Lin, Ning Liu, Mahdi Nazemi, Hongjia Li, Caiwen Ding, Yanzhi Wang, Massoud Pedram:
FFT-based deep learning deployment in embedded systems. DATE 2018: 1045-1050 - [c6]Caiwen Ding, Ao Ren, Geng Yuan, Xiaolong Ma, Jiayu Li, Ning Liu, Bo Yuan, Yanzhi Wang:
Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs. ACM Great Lakes Symposium on VLSI 2018: 353-358 - [i5]Ruizhe Cai, Ao Ren, Ning Liu, Caiwen Ding, Luhao Wang, Xuehai Qian, Massoud Pedram, Yanzhi Wang:
VIBNN: Hardware Acceleration of Bayesian Neural Networks. CoRR abs/1802.00822 (2018) - [i4]Caiwen Ding, Ao Ren, Geng Yuan, Xiaolong Ma, Jiayu Li, Ning Liu, Bo Yuan, Yanzhi Wang:
Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs. CoRR abs/1804.11239 (2018) - 2017
- [j1]Caiwen Ding, Ning Liu, Yanzhi Wang, Ji Li, Soroush Heidari, Jingtong Hu, Yongpan Liu:
Multisource Indoor Energy Harvesting for Nonvolatile Processors. IEEE Des. Test 34(3): 42-49 (2017) - [c2]Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo, Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, Xiaolong Ma, Yipeng Zhang, Jian Tang, Qinru Qiu, Xue Lin, Bo Yuan:
CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices. MICRO 2017: 395-408 - [i2]Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo, Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, Xiaolong Ma, Yipeng Zhang, Jian Tang, Qinru Qiu, Xue Lin, Bo Yuan:
CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices. CoRR abs/1708.08917 (2017) - [i1]Sheng Lin, Ning Liu, Mahdi Nazemi, Hongjia Li, Caiwen Ding, Yanzhi Wang, Massoud Pedram:
FFT-Based Deep Learning Deployment in Embedded Systems. CoRR abs/1712.04910 (2017) - 2016
- [c1]Ning Liu, Caiwen Ding, Yanzhi Wang, Jingtong Hu:
Neural Network-based Prediction Algorithms for In-Door Multi-Source Energy Harvesting System for Non-Volatile Processors. ACM Great Lakes Symposium on VLSI 2016: 275-280
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