The two organizations have joined efforts in the Machine Learning for Wireless Networking Systems (MLWiNS) program to support research that focuses on enabling ultra-dense wireless systems and architectures that meet the throughput, latency, security, and reliability requirements of future applications. At the same time, the program will also target research on distributed machine learning computations over wireless edge networks, to enable a broad range of new applications.
"Since 2015, Intel and NSF have collectively contributed more than $30 million to support science and engineering research in emerging areas of technology." says Gabriela Cruz Thompson, director of university research and collaborations at Intel Labs. "MLWiNS is the next step in this collaboration and has the promise to enable future wireless systems that serve the world’s rising demand for pervasive, intelligent devices."
The program is aimed at addressing the increasing demand for advanced connected services and devices. Machine learning shows great potential for managing the size and complexity of the wireless networks required to support such applications – addressing the demand for capacity and coverage while maintaining the stringent and diverse quality of service expected from network users.
At the same time, say the organizations, sophisticated networks and devices create an opportunity for machine learning services and computation to be deployed closer to where the data is generated, which alleviates bandwidth, privacy, latency and scalability concerns to move data to the cloud.
“5G and Beyond networks need to support throughput, density and latency requirements that are orders of magnitudes higher than what current wireless networks can support, and they also need to be secure and energy-efficient,” says Margaret Martonosi, assistant director for computer and information science and engineering at NSF. "The MLWiNS program was designed to stimulate novel machine learning research that can help meet these requirements – the awards announced today seek to apply innovative machine learning techniques to future wireless network designs to enable such advances and capabilities."