# Enhancing Reliability of Neural Networks at the Edge: Inverted Normalization with Stochastic Affine Transformations

arXiv: https://arxiv.org/abs/2401.12416 · HTML: https://mlsanity.com/paper/2401.12416/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [PACT: Parameterized Clipping Activation for Quantized Neural Networks](https://mlsanity.com/paper/1805.06085/) (2018)
- [Forward and Backward Information Retention for Accurate Binary Neural Networks](https://mlsanity.com/paper/1909.10788/) (2019)
- [Spatial-SpinDrop: Spatial Dropout-based Binary Bayesian Neural Network with Spintronics Implementation](https://mlsanity.com/paper/2306.10185/) (2023)
- [BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture](https://mlsanity.com/paper/2210.01795/) (2022)

## Abstract (arXiv)

Bayesian Neural Networks (BayNNs) naturally provide uncertainty in their predictions, making them a suitable choice in safety-critical applications. Additionally, their realization using memristor-based in-memory computing (IMC) architectures enables them for resource-constrained edge applications. In addition to predictive uncertainty, however, the ability to be inherently robust to noise in computation is also essential to ensure functional safety. In particular, memristor-based IMCs are susceptible to various sources of non-idealities such as manufacturing and runtime variations, drift, and failure, which can significantly reduce inference accuracy. In this paper, we propose a method to inherently enhance the robustness and inference accuracy of BayNNs deployed in IMC architectures. To achieve this, we introduce a novel normalization layer combined with stochastic affine transformations. Empirical results in various benchmark datasets show a graceful degradation in inference accuracy, with an improvement of up to 58.11%.