NetHD: Neurally Inspired Integration of Communication and Learning in Hyperspace

Abstract

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.

Keywords

• artificial intelligent
• channel computing
• energy efficiency
• hyperdimensional computing