Details

Autor(en) / Beteiligte

• Barkam, Hamza Errahmouni
• Jeon, SungHeon Eavn
• Yun, Sanggeon
• Yeung, Calvin
• Zou, Zhuowen
• Jiao, Xun
• Srinivasa, Narayan
• Imani, Mohsen

Titel

Invited Paper: Hyperdimensional Computing for Resilient Edge Learning

Ist Teil von

• 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2023, p.1-8

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEL

Beschreibungen/Notizen

• Recent strides in deep learning have yielded impres-sive practical applications such as autonomous driving, natural language processing, and graph reasoning. However, the sus-ceptibility of deep learning models to subtle input variations, which stems from device imperfections and non-idealities, or adversarial attacks on edge devices, presents a critical challenge. These vulnerabilities hold dual significance-security concerns in critical applications and insights into human-machine sen-sory alignment. Efforts to enhance model robustness encounter resource constraints in the edge and the black box nature of neural networks, hindering their deployment on edge devices. This paper focuses on algorithmic adaptations inspired by the human brain to address these challenges. Hyper Dimensional Computing (HDC), rooted in neural principles, replicates brain functions while enabling efficient, noise-tolerant computation. HDC leverages high-dimensional vectors to encode information, seamlessly blending learning and memory functions. Its trans-parency empowers practitioners, enhancing both robustness and understanding of deployed models. In this paper, we introduce the first comprehensive study that compares the robustness of HDC to white-box malicious attacks to that of deep neural network (DNN) models and the first HDC gradient-based attack in the literature. We develop a framework that enables HDC models to generate gradient-based adversarial examples using state-of-the-art techniques applied to DNNs. Our evaluation shows that our HDC model provides, on average, 19.9% higher robustness than DNNs to adversarial samples and up to 90% robustness improvement against random noise on the weights of the model compared to the DNN.