RTML: Small: Achieving Real-Time and Energy-efficient Computing for 5G Networks (ARTEN): A Deep Reservoir Computing Approach

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

Fueled by the popularity of smartphones as well as the upcoming deployment of the fifth-generation (5G) mobile broadband wireless networks, mobile data traffic is projected to have an explosive growth in the near future. In 5G wireless networks, enhanced mobile broadband (eMBB) and massive machine type communications (mMTC) are regarded as two primary use cases for 5G radios where eMBB supports stable connections with very high data rates for low-speed mobile users as well as moderate data rates for high-speed mobile users, and eMTC supports a massive number of Internet of Things (IoT) devices which are only sporadically active to transmit small data payloads. In this project, novel machine-learning-based hardware and customized efficient training algorithms tailored towards 5G eMBB and mMTC will be introduced to achieve real-time and energy-efficient computing for 5G wireless networks. The proposed research will involve training of both graduate and undergraduate students in circuit design, computing, communications, and networking. Through the development of application-oriented projects and various outreach activities supported by Virginia Tech's outreach programs, undergraduates including students from underrepresented groups in STEM will have opportunities to develop creative and independent problem-solving skills. The objective of this project is to develop a novel deep-learning-based hardware and software co-design platform to achieve real-time and energy-efficient computing for 5G wireless networks focusing on eMBB and mMTC. To be specific, 1) deep-learning-based integrated circuits and architectures will be designed to achieve real-time and energy-efficient learning; and 2) hardware-software-algorithm co-design with customized low-complexity online training algorithms will be introduced to realize high data rate symbol detection for eMBB as well as to realize distributed contention-based random-access strategies for mMTC. Both software and hardware testbeds will be developed to evaluate proposed hardware designs and learning algorithms. The success of the project will potentially revolutionize the telecommunication industry by incorporating machine learning and deep learning to the future 5G wireless devices and networks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.