RelHD: A Graph-based Learning on FeFET with Hyperdimensional Computing

Kang, Jaeyoung; Zhou, Minxuan; Bhansali, Abhinav; Xu, Weihong; Thomas, Anthony; Rosing, Tajana

doi:10.1109/iccd56317.2022.00087

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…where n is the number of windows within an image. We bind (via multiplication) each encoded W i by a random D-dimensional ID vector to distinguish between different win-dow locations [35]. An image of size m × m will have n = ( w(m−w) s…”

Section: Software Design: Hdc-based Classifiermentioning

confidence: 99%

Enhanced Noise-Resilient Pressure Mat System Based on Hyperdimensional Computing

Asgarinejad,

Yu,

Jiang

et al. 2024

Sensors

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Traditional systems for indoor pressure sensing and human activity recognition (HAR) rely on costly, high-resolution mats and computationally intensive neural network-based (NN-based) models that are prone to noise. In contrast, we design a cost-effective and noise-resilient pressure mat system for HAR, leveraging Velostat for intelligent pressure sensing and a novel hyperdimensional computing (HDC) classifier that is lightweight and highly noise resilient. To measure the performance of our system, we collected two datasets, capturing the static and continuous nature of human movements. Our HDC-based classification algorithm shows an accuracy of 93.19%, improving the accuracy by 9.47% over state-of-the-art CNNs, along with an 85% reduction in energy consumption. We propose a new HDC noise-resilient algorithm and analyze the performance of our proposed method in the presence of three different kinds of noise, including memory and communication, input, and sensor noise. Our system is more resilient across all three noise types. Specifically, in the presence of Gaussian noise, we achieve an accuracy of 92.15% (97.51% for static data), representing a 13.19% (8.77%) improvement compared to state-of-the-art CNNs.

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Section: Software Design: Hdc-based Classifiermentioning

confidence: 99%

Enhanced Noise-Resilient Pressure Mat System Based on Hyperdimensional Computing

Asgarinejad,

Yu,

Jiang

et al. 2024

Sensors

Self Cite

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“…In general, HDC employs its unique data type in the hyperdimensional space -hypervectors, which are ultralong vectors and usually have a dimensionality d of a thousand bits, e.g., d = 10, 000. Many data structures, such as letters [4][5][6], signals [7][8][9][10][11], graphs [12][13][14],…”

Section: Introductionmentioning

confidence: 99%

Robust Clustering Using Hyperdimensional Computing

Ge,

Parhi

2024

IEEE Open J. Circuits Syst.

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This paper addresses the clustering of data in the hyperdimensional computing (HDC) domain. In prior work, an HDC-based clustering framework, referred to as HDCluster, has been proposed. However, the performance of the existing HDCluster is not robust. The performance of HDCluster is degraded as the hypervectors for the clusters are chosen at random during the initialization step. To overcome this bottleneck, we assign the initial cluster hypervectors by exploring the similarity of the encoded data, referred to as query hypervectors. Intracluster hypervectors have a higher similarity than intercluster hypervectors. Harnessing the similarity results among query hypervectors, this paper proposes four HDCbased clustering algorithms: similarity-based k-means, equal bin-width histogram, equal bin-height histogram, and similarity-based affinity propagation. Experimental results illustrate that: (i) Compared to the existing HDCluster, our proposed HDC-based clustering algorithms can achieve better accuracy, more robust performance, fewer iterations, and less execution time. Similarity-based affinity propagation outperforms the other three HDC-based clustering algorithms on eight datasets by 2% ∼ 38% in clustering accuracy. (ii) Even for one-pass clustering, i.e., without any iterative update of the cluster hypervectors, our proposed algorithms can provide more robust clustering accuracy than HDCluster. (iii) Over eight datasets, five out of eight can achieve higher or comparable accuracy when projected onto the hyperdimensional space. Traditional clustering is more desirable than HDC when the number of clusters, k, is large.

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