Intel and National Science Foundation Invest in Wireless-Specific Machine Learning Edge Research
June 25 2020 - 10:00AM
Business Wire
What’s New: Today, Intel and the National Science
Foundation (NSF) announced award recipients of joint funding for
research into the development of future wireless systems. The
Machine Learning for Wireless Networking Systems (MLWiNS) program
is the latest in a series of joint efforts between the two partners
to support research that accelerates innovation with the focus of
enabling ultra-dense wireless systems and architectures that meet
the throughput, latency and reliability requirements of future
applications. In parallel, the program will 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. 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.” – Gabriela Cruz Thompson, director
of university research and collaborations at Intel Labs
Why It’s Important: As demand for advanced connected
services and devices grows, future wireless networks will need to
meet the challenging density, latency, throughput and security
requirements these applications will require. Machine learning
shows great potential to manage the size and complexity of such
networks – addressing the demand for capacity and coverage while
maintaining the stringent and diverse quality of service expected
from network users. At the same time, 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,” said 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.”
What Will Be Researched: Through MLWiNS, Intel and NSF
will fund research with the goal of driving new wireless system and
architecture design, increasing the utilization of sparse spectrum
resources and enhancing distributed machine learning computation
over wireless edge networks. Grant winners will conduct research
across multiple areas of machine learning and wireless networking.
Key focus areas and project examples include:
Reinforcement learning for wireless
networks: Research teams from the University of Virginia and
Penn State University will study reinforcement learning for
optimizing wireless network operation, focusing on tackling
convergence issues, leveraging knowledge-transfer methods to reduce
the amount of training data necessary, and bridging the gap between
model-based and model-free reinforcement learning through an
episodic approach.
Federated learning for edge
computing:
Researchers from the University of North
Carolina at Charlotte will explore methods to speed up multi-hop
federated learning over wireless communications, allowing multiple
groups of devices to collaboratively train a shared global model
while keeping their data local and private. Unlike classical
federated learning systems that utilize single-hop wireless
communications, multi-hop system updates need to go through
multiple noisy and interference-rich wireless links, which can
result in slower updates. Researchers aim to overcome this
challenge by developing a novel wireless multi-hop federated
learning system with guaranteed stability, high accuracy and a fast
convergence speed by systematically addressing the challenges of
communication latency, and system and data heterogeneity.
Researchers from the Georgia Institute of
Technology will analyze and design federated and collaborative
machine-learning training and inference schemes for edge computing,
with the goal of increasing efficiency over wireless networks. The
team will address challenges with real-time deep learning at the
edge, including limited and dynamic wireless channel bandwidth,
unevenly distributed data across edge devices and on-device
resource constraints.
Research from the University of Southern
California and the University of California, Berkeley will focus on
a coding-centric approach to enhance federated learning over
wireless communications. Specifically, researchers will work to
tackle the challenges of dealing with non-independent and
identically distributed data, and heterogeneous resources at the
wireless edge, and minimizing upload bandwidth costs from users,
while emphasizing issues of privacy and security when learning from
distributed data.
Distributed training across multiple edge
devices: Rice University researchers will work to train
large-scale centralized neural networks by separating them into a
set of independent sub-networks that can be trained on different
devices at the edge. This can reduce training time and complexity,
while limiting the impact on model accuracy.
Leveraging information theory and machine
learning to improve wireless network performance: Research
teams from the Massachusetts Institute of Technology and Virginia
Polytechnic Institute and State University will collaborate to
explore the use of deep neural networks to address physical layer
problems of a wireless network. They will exploit information
theoretic tools in order to develop new algorithms that can better
address non-linear distortions and relax simplifying assumptions on
the noise and impairments encountered in wireless networks.
Deep learning from radio frequency
signatures: Researchers at Oregon State University will
investigate cross-layer techniques that leverage the combined
capabilities of transceiver hardware, wireless radio frequency (RF)
domain knowledge and deep learning to enable efficient wireless
device classification. Specifically, the focus will be on
exploiting RF signal knowledge and transceiver hardware impairments
to develop efficient deep learning-based device classification
techniques that are scalable with the massive and diverse numbers
of emerging wireless devices, robust against device signature
cloning and replication, and agnostic to environment and system
distortions.
About Award Winners and Project Descriptions: A full list
of award winners and project descriptions can be found in "Intel
and National Science Foundation Announce Future Wireless Systems
Research Award Recipients."
More Context: NSF/Intel Partnership on Machine Learning
for Wireless Networking Systems (MLWiNS) | Intel Labs (Press Kit) |
Artificial Intelligence at Intel (Press Kit)
About Intel
Intel (Nasdaq: INTC) is an industry leader, creating
world-changing technology that enables global progress and enriches
lives. Inspired by Moore’s Law, we continuously work to advance the
design and manufacturing of semiconductors to help address our
customers’ greatest challenges. By embedding intelligence in the
cloud, network, edge and every kind of computing device, we unleash
the potential of data to transform business and society for the
better. To learn more about Intel’s innovations, go to
newsroom.intel.com and intel.com.
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Alexa Korkos 415-706-5783 Alexa.Korkos@intel.com
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