Side-Channel Extraction of Dataflow AI Accelerator Hardware Parameters Article Swipe
Guillaume Lomet
,
Rubén Salvador
,
Brice Colombier
,
Vincent Grosso
,
Olivier Sentieys
,
Cédric Killian
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/iolts65288.2025.11117043
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/iolts65288.2025.11117043
Dataflow neural network accelerators efficiently process AI tasks on FPGAs, with deployment simplified by ready-to-use frameworks and pre-trained models. However, this convenience makes them vulnerable to malicious actors seeking to reverse engineer valuable Intellectual Property (IP) through Side-Channel Attacks (SCA). This paper proposes a methodology to recover the hardware configuration of dataflow accelerators generated with the FINN framework. Through unsupervised dimensionality reduction, we reduce the computational overhead compared to the state-of-the-art, enabling lightweight classifiers to recover both folding and quantization parameters. We demonstrate an attack phase requiring only 337 ms to recover the hardware parameters with an accuracy of more than 95% and 421 ms to fully recover these parameters with an averaging of 4 traces for a FINN-based accelerator running a CNN, both using a random forest classifier on side-channel traces, even with the accelerator dataflow fully loaded. This approach offers a more realistic attack scenario than existing methods, and compared to SoA attacks based on tsfresh, our method requires 940x and 110x less time for preparation and attack phases, respectively, and gives better results even without averaging traces.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/iolts65288.2025.11117043
- OA Status
- green
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413320425
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413320425Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/iolts65288.2025.11117043Digital Object Identifier
- Title
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Side-Channel Extraction of Dataflow AI Accelerator Hardware ParametersWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-07Full publication date if available
- Authors
-
Guillaume Lomet, Rubén Salvador, Brice Colombier, Vincent Grosso, Olivier Sentieys, Cédric KillianList of authors in order
- Landing page
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https://doi.org/10.1109/iolts65288.2025.11117043Publisher landing page
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2506.15432Direct OA link when available
- Concepts
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Dataflow, Computer science, Side channel attack, Computer hardware, Channel (broadcasting), Extraction (chemistry), Parallel computing, Computer architecture, Embedded system, Algorithm, Computer network, Cryptography, Chemistry, ChromatographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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