Bayesian Inference Accelerator for Spiking Neural Networks

no code implementations • 27 Jan 2024 • Prabodh Katti, Anagha Nimbekar, Chen Li, Amit Acharyya, Bashir M. Al-Hashimi, Bipin Rajendran

Bayesian neural networks offer better estimates of model uncertainty compared to frequentist networks.

Bayesian Inference

Towards Efficient and Trustworthy AI Through Hardware-Algorithm-Communication Co-Design

no code implementations • 27 Sep 2023 • Bipin Rajendran, Osvaldo Simeone, Bashir M. Al-Hashimi

Artificial intelligence (AI) algorithms based on neural networks have been designed for decades with the goal of maximising some measure of accuracy.

Decision Making Uncertainty Quantification

Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity

no code implementations • 2 Feb 2023 • Prabodh Katti, Nicolas Skatchkovsky, Osvaldo Simeone, Bipin Rajendran, Bashir M. Al-Hashimi

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.

Bayesian Inference