8 papers
Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
Hyeongmeen Baik, Hamed Poursiami, Maryam Parsa +1
Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates spiking temporal proce…
qFHRR: Rethinking Fourier Holographic Reduced Representations through Quantized Phase and Integer Arithmetic
Shay Snyder, Hamed Poursiami, Maryam Parsa
Fourier Holographic Reduced Representations (FHRR) provide a compositional framework for encoding structured information with complex-valued hypervectors. FHRR rely on floating-poi…
ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding
Ziyi Liang, Hamed Poursiami, Zhishun Yang +5
Hyperdimensional Computing (HDC) offers a computationally efficient paradigm for neuromorphic learning. Yet, it lacks rigorous uncertainty quantification, leading to open decision…
VS-Graph: Scalable and Efficient Graph Classification Using Hyperdimensional Computing
Hamed Poursiami, Shay Snyder, Guojing Cong +2
Graph classification is a fundamental task in domains ranging from molecular property prediction to materials design. While graph neural networks (GNNs) achieve strong performance…
Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks
Hamed Poursiami, Ayana Moshruba, Maryam Parsa
As machine learning models become integral to security-sensitive applications, concerns over data leakage from adversarial attacks continue to rise. Model Inversion (MI) attacks po…
HyperGraphX: Graph Transductive Learning with Hyperdimensional Computing and Message Passing
Guojing Cong, Tom Potok, Hamed Poursiami +1
We present a novel algorithm, \hdgc, that marries graph convolution with binding and bundling operations in hyperdimensional computing for transductive graph learning. For predicti…