| Autor: |
Poduval, Prathyush P., Ni, Yang, Zou, Zhuowen, Ni, Kai, Imani, Mohsen |
| Zdroj: |
Advanced Intelligent Systems (2640-4567); Jul2024, Vol. 6 Issue 7, p1-18, 18p |
| Abstrakt: |
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$N e t H D$, is proposed, which enables robust and efficient data communication/learning. NetHD$N e t H D$ 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$N e t H D$ provides a bit error rate comparable to that of state‐of‐the‐art modulation schemes while achieving 9.4 ×$\times$ faster and 27.8 ×$\times$ higher energy efficiency compared to state‐of‐the‐art deep learning systems. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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