8 citations · 24 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks
Prabodh Katti, Clement Ruah, Osvaldo Simeone +2
Bayesian Neural Networks (BNNs) provide superior estimates of uncertainty by generating an ensemble of predictive distributions. However, inference via ensembling is resource-inten…
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…
Xpikeformer: Hybrid Analog-Digital Hardware Acceleration for Spiking Transformers
Zihang Song, Prabodh Katti, Osvaldo Simeone +1
The integration of neuromorphic computing and transformers through spiking neural networks (SNNs) offers a promising path to energy-efficient sequence modeling, with the potential…
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…
Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks
Zihang Song, Prabodh Katti, Osvaldo Simeone +1
Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency.…
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…