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ORCIDFan Chen 0001
Person information
- affiliation: Indiana University Bloomington, IN, USA
- affiliation (PhD 2020): Duke University, Department of Electrical and Computer Engineering, Durham, NC, USA
Other persons with the same name
- Fan Chen — disambiguation page
- Fan Chen 0002 — Japan Advanced Institute of Science and Technology, Ishikawa-ken, Japan
- Fan Chen 0003
![[0000-0002-2909-0295]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-2909-0295")
— Southwest Jiaotong University, Sichuan Key Laboratory of Signal and Information Processing, Chengdu, China - Fan Chen 0004 — SSAL Inc., Greenbelt, USA (and 2 more)
- Fan Chen 0005 — Wuhan University, School of Electrical Engineering and Automation, China
- Fan Chen 0006 — Massachusetts Institute of Technology, MIT, Machine Learning Optimization, MA, USA (and 1 more)
- Fan Chen 0007 — Kent State University, Department of Computer Science, OH, USA
- Fan Chen 0008 — Tongji University, Shanghai, China
- Fan Chen 0009 — Nanjing Institute of Technology, School of Electric Power Engineering, Jiangsu Collaborative Innovation Center for Smart Distribution Network, China (and 1 more)
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2020 – today
- 2024
[c35]Ruhan Wang, Fahiz Baba-Yara, Fan Chen:
JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks. ASPDAC 2024: 121-126- [c34]Ahmad Faiz, Sotaro Kaneda, Ruhan Wang, Rita Chukwunyere Osi, Prateek Sharma, Fan Chen, Lei Jiang:
LLMCarbon: Modeling the End-to-End Carbon Footprint of Large Language Models. ICLR 2024 - [c33]Zhenxiao Fu, Min Yang, Cheng Chu, Yilun Xu, Gang Huang, Fan Chen:
QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines. IJCNN 2024: 1-8 - [i13]Cheng Chu, Zhenxiao Fu, Yilun Xu, Gang Huang, Hausi A. Müller, Fan Chen, Lei Jiang:
TITAN: A Distributed Large-Scale Trapped-Ion NISQ Computer. CoRR abs/2402.11021 (2024) - [i12]Zhenxiao Fu, Min Yang, Cheng Chu, Yilun Xu, Gang Huang, Fan Chen:
QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines. CoRR abs/2403.10790 (2024) - [i11]Ahmad Faiz, Shahzeen Attari, Gayle Buck, Fan Chen, Lei Jiang:
IoTCO2: Assessing the End-To-End Carbon Footprint of Internet-of-Things-Enabled Deep Learning. CoRR abs/2403.10984 (2024) - [i10]Ruhan Wang, Fahiz Baba-Yara, Fan Chen:
JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks. CoRR abs/2403.11048 (2024) - 2023
- [j1]Farzaneh Zokaee, Fan Chen, Guangyu Sun, Lei Jiang:
Sky-Sorter: A Processing-in-Memory Architecture for Large-Scale Sorting. IEEE Trans. Computers 72(2): 480-493 (2023) - [c32]Cheng Chu, Lei Jiang, Martin Swany, Fan Chen:
QTROJAN: A Circuit Backdoor Against Quantum Neural Networks. ICASSP 2023: 1-5 - [c31]Cheng Chu, Grant Skipper, Martin Swany, Fan Chen:
IQGAN: Robust Quantum Generative Adversarial Network for Image Synthesis On NISQ Devices. ICASSP 2023: 1-5 - [c30]Mengxin Zheng, Fan Chen, Lei Jiang, Qian Lou:
PriML: An Electro-Optical Accelerator for Private Machine Learning on Encrypted Data. ISQED 2023: 1-7 - [c29]Cheng Chu, Fan Chen, Philip Richerme, Lei Jiang:
QDoor: Exploiting Approximate Synthesis for Backdoor Attacks in Quantum Neural Networks. QCE 2023: 1098-1106 - [c28]Cheng Chu, Lei Jiang, Fan Chen:
CryptoQFL: Quantum Federated Learning on Encrypted Data. QCE 2023: 1231-1237 - [c27]Linghao Song, Fan Chen, Hai Li, Yiran Chen:
ReFloat: Low-Cost Floating-Point Processing in ReRAM for Accelerating Iterative Linear Solvers. SC 2023: 75:1-75:15 - [i9]Cheng Chu, Lei Jiang, Martin Swany, Fan Chen:
QTrojan: A Circuit Backdoor Against Quantum Neural Networks. CoRR abs/2302.08090 (2023) - [i8]Cheng Chu, Lei Jiang, Fan Chen:
CryptoQFL: Quantum Federated Learning on Encrypted Data. CoRR abs/2307.07012 (2023) - [i7]Cheng Chu, Fan Chen, Philip Richerme, Lei Jiang:
QDoor: Exploiting Approximate Synthesis for Backdoor Attacks in Quantum Neural Networks. CoRR abs/2307.09529 (2023) - [i6]Ahmad Faiz, Sotaro Kaneda, Ruhan Wang, Rita Chukwunyere Osi, Parteek Sharma, Fan Chen, Lei Jiang:
LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models. CoRR abs/2309.14393 (2023) - 2022
- [c26]Samuel J. Engers, Cheng Chu, Dawen Xu, Ying Wang, Fan Chen:
MOCCA: A Process Variation Tolerant Systolic DNN Accelerator using CNFETs in Monolithic 3D. ACM Great Lakes Symposium on VLSI 2022: 379-382 - [c25]Cheng Chu, Nai-Hui Chia, Lei Jiang, Fan Chen:
QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates. ISLPED 2022: 4:1-4:6 - [c24]Cheng Chu, Dawen Xu, Ying Wang, Fan Chen:
Canopy: A CNFET-based Process Variation Aware Systolic DNN Accelerator. ISLPED 2022: 24:1-24:6 - [c23]Mengxin Zheng, Qian Lou, Fan Chen, Lei Jiang, Yongxin Zhu:
CryptoLight: An Electro-Optical Accelerator for Fully Homomorphic Encryption. NANOARCH 2022: 19:1-19:2 - [i5]Cheng Chu, Nai-Hui Chia, Lei Jiang, Fan Chen:
QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates. CoRR abs/2206.01345 (2022) - [i4]Mengxin Zheng, Qian Lou, Fan Chen, Lei Jiang, Yongxin Zhu:
CryptoLight: An Electro-Optical Accelerator for Fully Homomorphic Encryption. CoRR abs/2211.13780 (2022) - 2021
- [c22]Fan Chen, Linghao Song, Hai Helen Li, Yiran Chen:
RAISE: A Resistive Accelerator for Subject-Independent EEG Signal Classification. DATE 2021: 340-343 - [c21]Fan Chen, Linghao Song, Hai Li, Yiran Chen:
Marvel: A Vertical Resistive Accelerator for Low-Power Deep Learning Inference in Monolithic 3D. DATE 2021: 1240-1245 - [c20]Cheng Chu, Fan Chen, Dawen Xu, Ying Wang:
RECOIN: A Low-Power Processing-in-ReRAM Architecture for Deformable Convolution. ACM Great Lakes Symposium on VLSI 2021: 235-240 - [c19]Yitu Wang, Zhenhua Zhu, Fan Chen, Mingyuan Ma, Guohao Dai, Yu Wang, Hai Li, Yiran Chen:
Rerec: In-ReRAM Acceleration with Access-Aware Mapping for Personalized Recommendation. ICCAD 2021: 1-9 - [c18]Fan Chen:
PUFFIN: An Efficient DNN Training Accelerator for Direct Feedback Alignment in FeFET. ISLPED 2021: 1-6 - [c17]Farzaneh Zokaee, Bing Li, Fan Chen:
FeFET-based Process-in-Memory Architecture for Low-Power DNN Training. NANOARCH 2021: 1-6 - 2020
- [b1]Fan Chen:
In-Memory Computing Architecture for Deep Learning Acceleration. Duke University, Durham, NC, USA, 2020 - [c16]Fan Chen, Linghao Song, Hai Helen Li, Yiran Chen:
PARC: A Processing-in-CAM Architecture for Genomic Long Read Pairwise Alignment using ReRAM. ASP-DAC 2020: 175-180 - [c15]Linghao Song, Fan Chen, Yiran Chen, Hai Helen Li:
Parallelism in Deep Learning Accelerators. ASP-DAC 2020: 645-650 - [c14]Yitu Wang, Fan Chen, Linghao Song, Chuanjin Richard Shi, Hai Helen Li, Yiran Chen:
ReBoc: Accelerating Block-Circulant Neural Networks in ReRAM. DATE 2020: 1472-1477 - [c13]Linghao Song, Fan Chen, Youwei Zhuo, Xuehai Qian, Hai Li, Yiran Chen:
AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerators. HPCA 2020: 342-355 - [i3]Linghao Song, Fan Chen, Xuehai Qian, Hai Li, Yiran Chen:
Low-Cost Floating-Point Processing in ReRAM for Scientific Computing. CoRR abs/2011.03190 (2020)
2010 – 2019
- 2019
- [c12]Paul Bogdan, Fan Chen, Aryan Deshwal, Janardhan Rao Doppa, Biresh Kumar Joardar, Hai (Helen) Li, Shahin Nazarian, Linghao Song, Yao Xiao:
Taming extreme heterogeneity via machine learning based design of autonomous manycore systems. CODES+ISSS 2019: 21:1-21:10 - [c11]Fan Chen, Linghao Song, Hai Helen Li, Yiran Chen:
ZARA: A Novel Zero-free Dataflow Accelerator for Generative Adversarial Networks in 3D ReRAM. DAC 2019: 133 - [c10]Fan Chen, Linghao Song, Hai (Helen) Li:
Efficient Process-in-Memory Architecture Design for Unsupervised GAN-based Deep Learning using ReRAM. ACM Great Lakes Symposium on VLSI 2019: 423-428 - [c9]Linghao Song, Fan Chen, Steven R. Young, Catherine D. Schuman, Gabriel N. Perdue, Thomas E. Potok:
Deep Learning for Vertex Reconstruction of Neutrino-nucleus Interaction Events with Combined Energy and Time Data. ICASSP 2019: 3882-3886 - [c8]Fan Chen, Wei Wen, Linghao Song, Jingchi Zhang, Hai Helen Li, Yiran Chen:
How to Obtain and Run Light and Efficient Deep Learning Networks. ICCAD 2019: 1-5 - [c7]Jingyang Zhang, Huanrui Yang, Fan Chen, Yitu Wang, Hai Li:
Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment. EMC2@NeurIPS 2019: 1-5 - [i2]Linghao Song, Fan Chen, Steven R. Young, Catherine D. Schuman, Gabriel N. Perdue, Thomas E. Potok:
Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data. CoRR abs/1902.00743 (2019) - [i1]Jingyang Zhang, Huanrui Yang, Fan Chen, Yitu Wang, Hai Li:
Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment. CoRR abs/1909.08496 (2019) - 2018
- [c6]Fan Chen, Linghao Song, Yiran Chen:
ReGAN: A pipelined ReRAM-based accelerator for generative adversarial networks. ASP-DAC 2018: 178-183 - [c5]Fan Chen, Zheng Li, Wang Kang, Weisheng Zhao, Hai Li, Yiran Chen:
Process variation aware data management for magnetic skyrmions racetrack memory. ASP-DAC 2018: 221-226 - [c4]Bonan Yan, Fan Chen, Yaojun Zhang, Chang Song, Hai Li, Yiran Chen:
Exploring the opportunity of implementing neuromorphic computing systems with spintronic devices. DATE 2018: 109-112 - [c3]Bing Li, Linghao Song, Fan Chen, Xuehai Qian, Yiran Chen, Hai Helen Li:
ReRAM-based accelerator for deep learning. DATE 2018: 815-820 - [c2]Fan Chen, Hai Li:
EMAT: an efficient multi-task architecture for transfer learning using ReRAM. ICCAD 2018: 33 - [c1]Bing Li, Fan Chen, Wang Kang, Weisheng Zhao, Yiran Chen, Hai Li:
Design and Data Management for Magnetic Racetrack Memory. ISCAS 2018: 1-4
Coauthor Index
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