NetHD: Neurally Inspired Integration of Communication and Learning in Hyperspace

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 N⁢e⁢t⁢H⁢D, is proposed, which enables robust and efficient data communication/learning. 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 N⁢e⁢t⁢H⁢D 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.

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