Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Pelcat, Maxime"'
Publikováno v:
2022 IEEE Workshop on Signal Processing Systems (SiPS), 2022, pp. 1-6
Side-Channel Attacks (SCAs) exploit data correla-tion in signals leaked from devices to jeopardize confidentiality. Locating and synchronizing segments of interest in traces from Cryptographic Processes (CPs) is a key step of the attack. The most com
Externí odkaz:
http://arxiv.org/abs/2210.12059
Autor:
Fanni, Tiziana, Madronal, Daniel, Rubattu, Claudio, Sau, Carlo, Palumbo, Francesca, Juarez, Eduardo, Pelcat, Maxime, Sanz, Cesar, Raffo, Luigi
In the era of Cyber Physical Systems, designers need to offer support for run-time adaptivity considering different constraints, including the internal status of the system. This work presents a run-time monitoring approach, based on the Performance
Externí odkaz:
http://arxiv.org/abs/2103.01195
Graphics Processing Units (GPUs) are currently the dominating programmable architecture for Deep Learning (DL) accelerators. The adoption of Field Programmable Gate Arrays (FPGAs) in DL accelerators is however getting momentum. In this paper, we demo
Externí odkaz:
http://arxiv.org/abs/2102.01343
Publikováno v:
Workshop on Design and Architectures for Signal and Image Processing (DASIP), Jan 2021, Budapest, Hungary
Tangled Program Graph (TPG) is a reinforcement learning technique based on genetic programming concepts. On state-of-the-art learning environments, TPGs have been shown to offer comparable competence with Deep Neural Networks (DNNs), for a fraction o
Externí odkaz:
http://arxiv.org/abs/2012.08296
Autor:
Yuan, Shanxin, Timofte, Radu, Leonardis, Ales, Slabaugh, Gregory, Luo, Xiaotong, Zhang, Jiangtao, Qu, Yanyun, Hong, Ming, Xie, Yuan, Li, Cuihua, Xu, Dejia, Chu, Yihao, Sun, Qingyan, Liu, Shuai, Zong, Ziyao, Nan, Nan, Li, Chenghua, Kim, Sangmin, Nam, Hyungjoon, Kim, Jisu, Jeong, Jechang, Cheon, Manri, Yoon, Sung-Jun, Kang, Byungyeon, Lee, Junwoo, Zheng, Bolun, Liu, Xiaohong, Dai, Linhui, Chen, Jun, Cheng, Xi, Fu, Zhenyong, Yang, Jian, Lee, Chul, Vien, An Gia, Park, Hyunkook, Nathan, Sabari, Beham, M. Parisa, Roomi, S Mohamed Mansoor, Lemarchand, Florian, Pelcat, Maxime, Nogues, Erwan, Puthussery, Densen, S, Hrishikesh P, C V, Jiji, Sinha, Ashish, Zhao, Xuan
Publikováno v:
CVPRW 2020
This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demoireing is a difficult task of removing moire patterns from an image to
Externí odkaz:
http://arxiv.org/abs/2005.03155
Fully supervised deep-learning based denoisers are currently the most performing image denoising solutions. However, they require clean reference images. When the target noise is complex, e.g. composed of an unknown mixture of primary noises with unk
Externí odkaz:
http://arxiv.org/abs/2002.07487
Image denoising has recently taken a leap forward due to machine learning. However, image denoisers, both expert-based and learning-based, are mostly tested on well-behaved generated noises (usually Gaussian) rather than on real-life noises, making p
Externí odkaz:
http://arxiv.org/abs/1910.08328
This paper proposes an upgraded electro-magnetic side-channel attack that automatically reconstructs the intercepted data. A novel system is introduced, running in parallel with leakage signal interception and catching compromising data in real-time.
Externí odkaz:
http://arxiv.org/abs/1910.07201
Convolutional Neural Networks (CNNs) are computationally intensive algorithms that currently require dedicated hardware to be executed. In the case of FPGA-Based accelerators, we point-out in this work the challenge of Multi-Operand Adders (MOAs) and
Externí odkaz:
http://arxiv.org/abs/1807.00217
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of problems, ranging from speech recognition to image classification and segmentation. The large amount of processing required by CNNs calls for dedicated and
Externí odkaz:
http://arxiv.org/abs/1806.01683