# Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity

arXiv: https://arxiv.org/abs/2302.01302 · HTML: https://mlsanity.com/paper/2302.01302/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [The Bayesian Learning Rule](https://mlsanity.com/paper/2107.04562/) (2021)
- [The Case for Bayesian Deep Learning](https://mlsanity.com/paper/2001.10995/) (2020)
- [An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications](https://mlsanity.com/paper/1910.01059/) (2019)
- [Low-Power Neuromorphic Hardware for Signal Processing Applications](https://mlsanity.com/paper/1901.03690/) (2019)
- [VIBNN: Hardware Acceleration of Bayesian Neural Networks](https://mlsanity.com/paper/1802.00822/) (2018)
- [Bayesian Continual Learning via Spiking Neural Networks](https://mlsanity.com/paper/2208.13723/) (2022)
- [BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning](https://mlsanity.com/paper/2012.08300/) (2020)
- [MC2RAM: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference](https://mlsanity.com/paper/2003.02629/) (2020)
- [An Overview of In-memory Processing with Emerging Non-volatile Memory for Data-intensive Applications](https://mlsanity.com/paper/1906.06603/) (2019)

## Built on by (most cited first, 3 of 3)

- [Spatial-SpinDrop: Spatial Dropout-based Binary Bayesian Neural Network with Spintronics Implementation](https://mlsanity.com/paper/2306.10185/) (2023)
- [Distilling Calibration via Conformalized Credal Inference](https://mlsanity.com/paper/2501.06066/) (2025)
- [Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks](https://mlsanity.com/paper/2411.07902/) (2024)

## Abstract (arXiv)

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. The proposed architecture consists of separate weight and noise planes, in which PCM cells are configured and operated to represent the nominal values of weights and to generate the required noise for sampling, respectively. Using experimentally observed PCM noise characteristics, for the exemplary Breast Cancer Dataset classification problem, we obtain hardware accuracy and expected calibration error matching that of an 8-bit fixed-point (FxP8) implementation, with projected savings of over 9× in terms of core area transistor count.