8 citations · 24 across the 10 of their papers we have counts for
3 papers · 1 filter
Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology
Anagha Nimbekar, Prabodh Katti, Chen Li +3
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 computatio…
Bayesian Inference Accelerator for Spiking Neural Networks
Prabodh Katti, Anagha Nimbekar, Chen Li +3
Bayesian neural networks offer better estimates of model uncertainty compared to frequentist networks. However, inference involving Bayesian models requires multiple instantiations…
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
Prabodh Katti, Nicolas Skatchkovsky, Osvaldo Simeone +2
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 f…