no code implementations • 29 Apr 2024 • Daniel Volya, Andrey Nikitin, Prabhat Mishra
Constrained optimization plays a crucial role in the fields of quantum physics and quantum information science and becomes especially challenging for high-dimensional complex structure problems.
no code implementations • 19 Apr 2024 • Zhixin Pan, Emma Andrews, Laura Chang, Prabhat Mishra
Data augmentation is widely used to mitigate data bias in the training dataset.
no code implementations • 1 Nov 2023 • Hansika Weerasena, Prabhat Mishra
The proposed attack exploits spatial and temporal data reuse of the dataflow mapping on CNN accelerators and architectural hints to recover the structure of CNN models.
no code implementations • 27 Sep 2023 • Hansika Weerasena, Prabhat Mishra
We show that the existing anonymous routing is vulnerable to machine learning (ML) based flow correlation attacks on NoCs.
no code implementations • 4 May 2023 • Zhixin Pan, Prabhat Mishra
Extensive experimental evaluation demonstrates that proposed approach deployed on TPU can provide drastic improvement in interpretation time (39x on average) as well as energy efficiency (69x on average) compared to existing acceleration techniques.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
no code implementations • 18 May 2022 • Zhixin Pan, Prabhat Mishra
In this paper, we propose a novel backdoor attack based on effective learning and targeted utilization of reverse distribution.
no code implementations • 22 Mar 2021 • Zhixin Pan, Prabhat Mishra
(1) To the best of our knowledge, our proposed work is the first attempt in enabling hardware acceleration of explainable ML using TPUs.
no code implementations • 22 Mar 2021 • Zhixin Pan, Prabhat Mishra
One promising strategy to counter adversarial attacks is to utilize spectral normalization, which ensures that the trained model has low sensitivity towards the disturbance of input samples.