CNS Core: Small: Bringing the Cloud to the Masses: Models, Algorithms, and Systems Targeting Deep Learning for the IoT Era

University Of Texas At Austin, Austin TX

Investigators

Abstract

An estimated one trillion Internet-of-Things (IoT) devices are expected to impact multiple market segments by 2035. Such a rapid growth in IoT devices necessitates new breakthroughs in machine learning approaches to fully exploit the compute power offered by these devices. However, the significant computational requirements of existing deep learning approaches represent a major bottleneck for their large-scale deployment on hardware-constrained edge devices. To improve this state of affairs, this project seeks to develop new models, algorithms, and prototypes that can enable faster and computationally inexpensive inference on resource-constrained mobile/IoT devices. These new models can ultimately help preserve user’s privacy by conducting inference on a network of edge devices instead of sending data to the cloud. Through its proposed research and curriculum, this project enables a complete symbiosis between software, architectures, and system meant to transform not just parallel computing, but also real life applications ranging from data centers, to IoT and personal devices. Indeed, developing such “lightweight” machine learning systems can ultimately enable the democratization of access to application-specific hardware and make such systems widely available. All these contributions aim at helping transitioning traditional cloud computing into computing at the edge. Through curriculum related innovations, student competitions, and other outreach activities, this project is poised to raise awareness and increase participation of various underrepresented groups to this area of engineering 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.