2 citations · 2 across the 4 of their papers we have counts for
4 papers
Sparsity-Aware Optimization of In-Memory Bayesian Binary Neural Network Accelerators
Prabodh Katti, Bashir M. Al-Hashimi, Bipin Rajendran
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 tha…
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…
Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning
Robert O Shea, Prabodh Katti, Bipin Rajendran
Common artefacts such as baseline drift, rescaling, and noise critically limit the performance of machine learningbased automated ECG analysis and interpretation. This study propos…
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…