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2020 – today
- 2024
[j1]Saurav Maji
, Kyungmi Lee, Cheng Gongye, Yunsi Fei, Anantha P. Chandrakasan:
An Energy-Efficient Neural Network Accelerator With Improved Resilience Against Fault Attacks. IEEE J. Solid State Circuits 59(9): 3106-3116 (2024)- [c16]Cheng Gongye, Yunsi Fei:
One Flip Away from Chaos: Unraveling Single Points of Failure in Quantized DNN s. HOST 2024: 332-342 - [c15]Cheng Gongye, Yukui Luo, Xiaolin Xu, Yunsi Fei:
Side-Channel-Assisted Reverse-Engineering of Encrypted DNN Hardware Accelerator IP and Attack Surface Exploration. SP 2024: 4678-4695 - 2023
- [c14]Ruyi Ding, Cheng Gongye, Siyue Wang, A. Adam Ding, Yunsi Fei:
EMShepherd: Detecting Adversarial Samples via Side-channel Leakage. AsiaCCS 2023: 300-313 - [c13]Cheng Gongye, Yukui Luo, Xiaolin Xu, Yunsi Fei:
HammerDodger: A Lightweight Defense Framework against RowHammer Attack on DNNs. DAC 2023: 1-6 - [c12]Saurav Maji, Kyungmi Lee, Cheng Gongye, Yunsi Fei, Anantha P. Chandrakasan:
An Energy-Efficient Neural Network Accelerator with Improved Protections Against Fault-Attacks. ESSCIRC 2023: 233-236 - [i5]Ruyi Ding, Cheng Gongye, Siyue Wang, Aidong Adam Ding, Yunsi Fei:
EMShepherd: Detecting Adversarial Samples via Side-channel Leakage. CoRR abs/2303.15571 (2023) - 2022
- [c11]Ruyi Ding, Ziyue Zhang, Xiang Zhang, Cheng Gongye, Yunsi Fei, Aidong Adam Ding:
A Cross-Platform Cache Timing Attack Framework via Deep Learning. DATE 2022: 676-681 - [c10]Yukui Luo, Shijin Duan, Cheng Gongye, Yunsi Fei, Xiaolin Xu:
NNReArch: A Tensor Program Scheduling Framework Against Neural Network Architecture Reverse Engineering. FCCM 2022: 1-9 - [c9]Tianhong Xu, Cheng Gongye, Yunsi Fei:
Protected ECC Still Leaks: A Novel Differential-Bit Side-channel Power Attack on ECDH and Countermeasures. ACM Great Lakes Symposium on VLSI 2022: 9-14 - [c8]Xiang Zhang, Ziyue Zhang, Ruyi Ding, Cheng Gongye, Aidong Adam Ding, Yunsi Fei:
Ran$Net: An Anti-Ransomware Methodology based on Cache Monitoring and Deep Learning. ACM Great Lakes Symposium on VLSI 2022: 487-492 - [i4]Yukui Luo, Shijin Duan, Cheng Gongye, Yunsi Fei, Xiaolin Xu:
NNReArch: A Tensor Program Scheduling Framework Against Neural Network Architecture Reverse Engineering. CoRR abs/2203.12046 (2022) - 2021
- [c7]Yukui Luo, Cheng Gongye, Yunsi Fei, Xiaolin Xu:
DeepStrike: Remotely-Guided Fault Injection Attacks on DNN Accelerator in Cloud-FPGA. DAC 2021: 295-300 - [i3]Yukui Luo, Cheng Gongye, Yunsi Fei, Xiaolin Xu:
DeepStrike: Remotely-Guided Fault Injection Attacks on DNN Accelerator in Cloud-FPGA. CoRR abs/2105.09453 (2021) - 2020
- [c6]Cheng Gongye, Yunsi Fei, Thomas Wahl:
Reverse-Engineering Deep Neural Networks Using Floating-Point Timing Side-Channels. DAC 2020: 1-6 - [c5]Cheng Gongye, Hongjia Li, Xiang Zhang, Majid Sabbagh, Geng Yuan, Xue Lin, Thomas Wahl, Yunsi Fei:
New Passive and Active Attacks on Deep Neural Networks in Medical Applications. ICCAD 2020: 39:1-39:9 - [c4]Yukui Luo, Cheng Gongye, Shaolei Ren, Yunsi Fei, Xiaolin Xu:
Stealthy-Shutdown: Practical Remote Power Attacks in Multi - Tenant FPGAs. ICCD 2020: 545-552 - [c3]Yunsi Fei, Guang Gong, Cheng Gongye, Kalikinkar Mandal, Raghvendra Rohit, Tianhong Xu, Yunjie Yi, Nusa Zidaric:
Correlation Power Analysis and Higher-Order Masking Implementation of WAGE. SAC 2020: 593-614 - [i2]Yunsi Fei, Guang Gong, Cheng Gongye, Kalikinkar Mandal, Raghvendra Rohit, Tianhong Xu, Yunjie Yi, Nusa Zidaric:
Correlation Power Analysis and Higher-order Masking Implementation of WAGE. IACR Cryptol. ePrint Arch. 2020: 1202 (2020)
2010 – 2019
- 2019
- [c2]Pu Zhao, Siyue Wang, Cheng Gongye, Yanzhi Wang, Yunsi Fei, Xue Lin:
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks. DAC 2019: 165 - [c1]Majid Sabbagh, Cheng Gongye, Yunsi Fei, Yanzhi Wang:
Evaluating Fault Resiliency of Compressed Deep Neural Networks. ICESS 2019: 1-7 - [i1]Pu Zhao, Siyue Wang, Cheng Gongye, Yanzhi Wang, Yunsi Fei, Xue Lin:
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks. CoRR abs/1905.12032 (2019)
Coauthor Index
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last updated on 2024-11-11 22:24 CET by the dblp team
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