Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Katti, Prabodh"'
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
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
This paper introduces Xpikeformer, a hybrid analog-digital hardware architecture designed to accelerate spiking neural network (SNN)-based transformer models. By combining the energy efficiency and temporal dynamics of SNNs with the powerful sequence
Externí odkaz:
http://arxiv.org/abs/2408.08794
Common artefacts such as baseline drift, rescaling, and noise critically limit the performance of machine learningbased automated ECG analysis and interpretation. This study proposes Derived Peak (DP) encoding, a non-parametric method that generates
Externí odkaz:
http://arxiv.org/abs/2405.00724
Publikováno v:
2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS), 2024
Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency. Yet, the implementation of the attention mechanism using spiking signa
Externí odkaz:
http://arxiv.org/abs/2402.09109
Autor:
Katti, Prabodh, Nimbekar, Anagha, Li, Chen, Acharyya, Amit, Al-Hashimi, Bashir M., Rajendran, Bipin
Bayesian neural networks offer better estimates of model uncertainty compared to frequentist networks. However, inference involving Bayesian models requires multiple instantiations or sampling of the network parameters, requiring significant computat
Externí odkaz:
http://arxiv.org/abs/2401.15453
Autor:
Katti, Prabodh, Skatchkovsky, Nicolas, Simeone, Osvaldo, Rajendran, Bipin, Al-Hashimi, Bashir M.
Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems. However, conventional hardware realizations of
Externí odkaz:
http://arxiv.org/abs/2302.01302