The introduction of 6G Networks and its deep integration of Artificial Intelligence (AI) necessitates powerful and efficient hardware processors to manage massive data flows, ensure network reliability, and support demanding use cases. To overcome these challenges, the industry is shifting away from conventional processing architectures (CPUs and GPUs) toward specialized hardware accelerators. This paper reviews three critical categories of processing chips: Neuromorphic chips, Neural Processing Units (NPUs), and Tensor Processing Units (TPUs). It also reviews other emerging chips that are still relatively unknown such as Quantum Processing Units (QPUs) and Photonic Integrated Circuits (PICs). Specifically, this paper will review different research tasks and display tables of hardware efficiency and wireless system metrics.
- Open Access
- Review
A Review of AI Computing Hardware for Enabling 6G Wireless Networks: Neuromorphic Chips, NPUs, and TPUs
- Jason Lee *,
- Yu-Dong Yao
Author Information
Received: 30 Nov 2025 | Revised: 29 Jan 2026 | Accepted: 07 Jun 2026 | Published: 16 Jul 2026
Abstract
Keywords
neural processing unit (NPU) | tensor processing unit (TPU) | compute-in-memory (CIM) | spike neural networks (SNNs) | ultra-reliable low-latency communication (URLLC) | multi-processor system-on-chip (MPSoC) | photonic integrated circuits (PICs) | quantum computing | energy efficiency | systolic array | digital twinning | integrated sensing and communication (ISAC)
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