ArticleNature communications2025
The inherent adversarial robustness of analog in-memory computing.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Implementation of offset corrected AGAD algorithm on 130-nm CMOS technology-based RRAM array for analog neural network training.Science advances · 2026Article
- Coexisting Volatile and Nonvolatile Switching in 3D ALD-IGZO Vertical RRAM for Fully Hardware-Based Wide Reservoir Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- GA2AD: a generalized adaptive adversarial training framework considering surrounding hybrid risk field for autonomous driving.Nature communications · 2026Article
- Hybrid GNN-LSTM defense with differential privacy and secure multi-party computation for edge-optimized neuromorphic autonomous systems.Scientific reports · 2025Article
- Memristive In-Memory Object Detection with 128 Mb C-Doped GeAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A key challenge for deep neural network algorithms is their vulnerability to adversarial attacks. Inherently non-deterministic compute substrates, such as those based on analog in-memory computing, have been speculated to provide significant adversarial robustness when performing deep neural network inference. In this paper, we experimentally validate this conjecture for the first time on an analog in-memory computing chip based on phase change memory devices. We demonstrate higher adversarial robustness against different types of adversarial attacks when implementing an image classification network. Additional robustness is also observed when performing hardware-in-the-loop attacks, for which the attacker is assumed to have full access to the hardware. A careful study of the various noise sources indicate that a combination of stochastic noise sources (both recurrent and non-recurrent) are responsible for the adversarial robustness and that their type and magnitude disproportionately effects this property. Finally, it is demonstrated, via simulations, that when a much larger transformer network is used to implement a natural language processing task, additional robustness is still observed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.