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Joseph Clements

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2 papers

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AAAI Conference 2024 Conference Paper

Resource Efficient Deep Learning Hardware Watermarks with Signature Alignment

Joseph Clements
Yingjie Lao

Deep learning intellectual properties (IPs) are high-value assets that are frequently susceptible to theft. This vulnerability has led to significant interest in defending the field's intellectual properties from theft. Recently, watermarking techniques have been extended to protect deep learning hardware from privacy. These technique embed modifications that change the hardware's behavior when activated. In this work, we propose the first method for embedding watermarks in deep learning hardware that incorporates the owner's key samples into the embedding methodology. This improves our watermarks' reliability and efficiency in identifying the hardware over those generated using randomly selected key samples. Our experimental results demonstrate that by considering the target key samples when generating the hardware modifications, we can significantly increase the embedding success rate while targeting fewer functional blocks, decreasing the required hardware overhead needed to defend it.

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AAAI Conference 2022 Conference Paper

DeepHardMark: Towards Watermarking Neural Network Hardware

Joseph Clements
Yingjie Lao

This paper presents a framework for embedding watermarks into DNN hardware accelerators. Unlike previous works that have looked at protecting the algorithmic intellectual properties of deep learning systems, this work proposes a methodology for defending deep learning hardware. Our methodology embeds modifications into the hardware accelerator’s functional blocks that can be revealed with the rightful owner’s key DNN and corresponding key sample, verifying the legitimate owner. We propose an `p-ADMM based algorithm to co-optimize the watermark’s hardware overhead and impact on the design’s algorithmic functionality. We evaluate the performance of the hardware watermarking scheme on popular image classification models using various accelerator designs. Our results demonstrate that the proposed methodology effectively embeds watermarks while preserving the original functionality of the hardware architecture. Specifically, we can successfully embed watermarks into the deep learning hardware and reliably execute a ResNet ImageNet classifier with an accuracy degradation of only 0. 009%.

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