Zobrazeno 1 - 10
of 46 454
pro vyhledávání: '"Rajendran, A."'
Autor:
Melnychuk, Oleksandr, Giaccone, Bianca, Bornman, Nicholas, Cervantes, Raphael, Grassellino, Anna, Harnik, Roni, Kaplan, David E., Nahal, Geev, Pilipenko, Roman, Posen, Sam, Rajendran, Surjeet, Sushkov, Alexander O.
There are strong arguments that quantum mechanics may be nonlinear in its dynamics. A discovery of nonlinearity would hint at a novel understanding of the interplay between gravity and quantum field theory, for example. As such, experiments searching
Externí odkaz:
http://arxiv.org/abs/2411.09611
Bayesian Neural Networks (BNNs) provide superior estimates of uncertainty by generating an ensemble of predictive distributions. However, inference via ensembling is resource-intensive, requiring additional entropy sources to generate stochasticity w
Externí odkaz:
http://arxiv.org/abs/2411.07902
Bayesian Neural Networks (BNNs) provide principled estimates of model and data uncertainty by encoding parameters as distributions. This makes them key enablers for reliable AI that can be deployed on safety critical edge systems. These systems can b
Externí odkaz:
http://arxiv.org/abs/2411.07842
Inspired by biological processes, neuromorphic computing utilizes spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have
Externí odkaz:
http://arxiv.org/abs/2411.04728
Autor:
Qiu, Liang, Chi, Wenhao, Xing, Xiaohan, Rajendran, Praveenbalaji, Li, Mingjie, Jiang, Yuming, Pastor-Serrano, Oscar, Yang, Sen, Wang, Xiyue, Ji, Yuanfeng, Wen, Qiang
Precision therapy for liver cancer necessitates accurately delineating liver sub-regions to protect healthy tissue while targeting tumors, which is essential for reducing recurrence and improving survival rates. However, the segmentation of hepatic s
Externí odkaz:
http://arxiv.org/abs/2411.02815
Sequence models have demonstrated the ability to perform tasks like channel equalization and symbol detection by automatically adapting to current channel conditions. This is done without requiring any explicit optimization and by leveraging not only
Externí odkaz:
http://arxiv.org/abs/2410.23882
Autor:
Rostami, Mohamadreza, Chen, Chen, Kande, Rahul, Li, Huimin, Rajendran, Jeyavijayan, Sadeghi, Ahmad-Reza
Publikováno v:
IEEE Security & Privacy ( Volume: 22, Issue: 4, July-Aug. 2024)
Hardware-level memory vulnerabilities severely threaten computing systems. However, hardware patching is inefficient or difficult postfabrication. We investigate the effectiveness of hardware fuzzing in detecting hardware memory vulnerabilities and h
Externí odkaz:
http://arxiv.org/abs/2410.22561
Autor:
Rostami, Mohamadreza, Zeitouni, Shaza, Kande, Rahul, Chen, Chen, Mahmoody, Pouya, Jeyavijayan, Rajendran, Sadeghi, Ahmad-Reza
Microarchitectural attacks represent a challenging and persistent threat to modern processors, exploiting inherent design vulnerabilities in processors to leak sensitive information or compromise systems. Of particular concern is the susceptibility o
Externí odkaz:
http://arxiv.org/abs/2410.22555
Autor:
Nimbekar, Anagha, Katti, Prabodh, Li, Chen, Al-Hashimi, Bashir M., Acharyya, Amit, Rajendran, Bipin
Spiking Neural Networks (SNNs) have emerged as a promising approach to improve the energy efficiency of machine learning models, as they naturally implement event-driven computations while avoiding expensive multiplication operations. In this paper,
Externí odkaz:
http://arxiv.org/abs/2410.16298
Autor:
Bouchoucha, Rached, Yahmed, Ahmed Haj, Patil, Darshan, Rajendran, Janarthanan, Nikanjam, Amin, Chandar, Sarath, Khomh, Foutse
Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However, like any other software system, DRL-based software systems are susceptible to faults that pose unique challe
Externí odkaz:
http://arxiv.org/abs/2410.04322