Reconfigurable Semiconductor Architectures For AI-Enhanced Wireless Communication Networks

Abstract

Recent advances in artificial intelligence (AI) have enabled drastic performance enhancement for distributed wireless communication networks, especially in terms of user experience. The rapid evolution of the mobile communication modules, particularly with the adoption of massive antenna arrays in the new 5G radio access networks, further creates opportunities to incorporate AI techniques to overturn the traditional end-to-end signal processing architecture. Integrating AI techniques directly in the communication functionality has indeed been found to cast new perspectives on several radio resource allocation and signal processing problems. The involvement of machine learning in the wireless communication domain also potentially enhances the switch from the traditional model-based designs to more flexible and dynamic data-based designs. Future AI-based mobile communication systems tend to demand large computation and energy resources. However, the current mobile SoC chips are mostly designed with a traditional fixed architecture. This challenge further bridges research interests into reconfigurable semiconductor architectures for mobile AI applications. This chapter begins by introducing the usage of AI in the wireless communication domain, with a specific focus on how re-thinking the basic radio signal processing and radio resource management designs could improve the mobile user experience and reduce the design pipeline for special use-case functional modules. The chapter continues to summarize the principles to achieve mobile functions tasks for wireless communication and AI, as well as the needed chip architecture and design considerations. Based on these principles, the architecture and design considerations for future AI-enhanced communication SoCs are elaborated. Finally, we present detailed cases along the current AI-enabled mobile functionality designs, and provide possible future outlook and open issues. These corresponding discussions to develop a better understanding of AI-mobile systems co-design will hopefully advance the research interests in future novel chip designs to support the power-hungry wireless communication and AI applications.