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

• URL: http://arxiv.org/abs/2401.12416v1
• Date: Tue, 23 Jan 2024 00:27:31 GMT
• Title: Enhancing Reliability of Neural Networks at the Edge: Inverted Normalization with Stochastic Affine Transformations
• Authors: Soyed Tuhin Ahmed, Kamal Danouchi, Guillaume Prenat, Lorena Anghel, Mehdi B. Tahoori
• Abstract summary: We propose a method to inherently enhance the robustness and inference accuracy of BayNNs deployed in in-memory computing architectures. Empirical results show a graceful degradation in inference accuracy, with an improvement of up to $58.11%$.
• Score: 0.22499166814992438
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 $58.11\%$.

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