Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity

Understand

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.

• However, conventional hardware realizations of BNNs are resource intensive, requiring the implementation of random number generators for synaptic sampling.
• Owing to their inherent stochasticity during programming and read operations, nanoscale memristive devices can be directly leveraged for sampling, without the need for additional hardware resources.
• In this paper, we introduce a novel Phase Change Memory (PCM)-based hardware implementation for BNNs with binary synapses.