Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity. - Prabodh Katti; Nicolas Skatchkovsky; Osvaldo Simeone; Bipin Rajendran; Bashir M. Al-Hashimi (ISCAS, 2023)

Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks. - Zihang Song; Prabodh Katti; Osvaldo Simeone; Bipin Rajendran (AICAS, 2024)

Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning. - Robert O'Shea; Prabodh Katti; Bipin Rajendran (EMBC, 2024)

Bayesian Inference Accelerator for Spiking Neural Networks. - Prabodh Katti; Anagha Nimbekar; Chen Li; Amit Acharyya; Bashir M. Al-Hashimi; Bipin Rajendran (ISCAS, 2024)

Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology. - Anagha Nimbekar; Prabodh Katti; Chen Li; Bashir M. Al-Hashimi; Amit Acharyya; Bipin Rajendran (SOCC, 2024)

Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning. - Robert O. Shea; Prabodh Katti; Bipin Rajendran (CoRR, 2024)

Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks. - Prabodh Katti; Clement Ruah; Osvaldo Simeone; Bashir M. Al-Hashimi; Bipin Rajendran (CoRR, 2024)

Sparsity-Aware Optimization of In-Memory Bayesian Binary Neural Network Accelerators. - Prabodh Katti; Bashir M. Al-Hashimi; Bipin Rajendran (ISCAS, 2025)

Xpikeformer: Hybrid Analog-Digital Hardware Acceleration for Spiking Transformers. - Zihang Song; Prabodh Katti; Osvaldo Simeone; Bipin Rajendran (IEEE Trans. Very Large Scale Integr. Syst., 2025)