| Autor: |
Prathyush P. Poduval, Yang Ni, Zhuowen Zou, Kai Ni, Mohsen Imani |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Advanced Intelligent Systems, Vol 6, Iss 7, Pp n/a-n/a (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2640-4567 |
| DOI: |
10.1002/aisy.202300841 |
| Popis: |
The 6G network, the next‐generation communication system, is envisaged to provide unprecedented experience through hyperconnectivity involving everything. The communication should hold artificial intelligence‐centric network infrastructures as interconnecting a swarm of machines. However, existing network systems use orthogonal modulation and costly error correction code; they are very sensitive to noise and rely on many processing layers. These schemes impose significant overhead on low‐power internet of things devices connected to noisy networks. Herein, a hyperdimensional network‐based system, called NetHD, is proposed, which enables robust and efficient data communication/learning. NetHD exploits a redundant and holographic representation of hyperdimensional computing (HDC) to design highly robust data modulation, enabling two functionalities on transmitted data: 1) an iterative decoding method that translates the vector back to the original data without error correction mechanisms, or 2) a native hyperdimensional learning technique on transmitted data with no need for costly data decoding. A hardware accelerator that supports both data decoding and hyperdimensional learning using a unified accelerator is also developed. The evaluation shows that NetHD provides a bit error rate comparable to that of state‐of‐the‐art modulation schemes while achieving 9.4 × faster and 27.8 × higher energy efficiency compared to state‐of‐the‐art deep learning systems. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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