Can Short Hypervectors Drive Feature-Rich GNNs? Strengthening the Graph Representation of Hyperdimensional Computing for Memory-efficient GNNs

Hyperdimensional computing (HDC) based GNNs are significantly advancing the brain-like cognition in terms of mathematical rigorousness and computational tractability. However, the researches in this field seem to have a "long vector consensus" that the length of HDC-hypervectors must be de...

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Vydané v:	2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori:	Wang, Jihe, Han, Yuxi, Wang, Danghui
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 22.06.2025
Predmet:	Accuracy Cognition Design automation Distortion Feature extraction GNN graph representation hyperdimensional computing memory Memory management Training Vectors
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