1. Enhancing Reliability of Neural Networks at the Edge: Inverted Normalization with Stochastic Affine Transformations
- Author
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Ahmed, Soyed Tuhin, Danouchi, Kamal, Prenat, Guillaume, Anghel, Lorena, and Tahoori, Mehdi B.
- Subjects
Computer Science - Machine Learning ,Computer Science - Hardware Architecture ,Computer Science - Emerging Technologies - Abstract
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 $58.11\%$.
- Published
- 2024