The ever-increasing number of Internet of Things (IoT) devices generate large quantities of raw data that need to be processed and analyzed in real time. Since conducting computationally expensive tasks, such as computer vision and natural language processing, is often a challenge for IoT devices, most of their computations are currently offloaded to cloud servers. However, this offloading leads to an increased risk for privacy as well as a dependency on network connectivity. To solve this challenge, the project utilizes the distributed computing power of already connected IoT devices to perform high computing power applications in real time.
The project is composed of three tasks. First is the development of distributed machine learning (ML) systems for multiple IoT devices. The project will involve studying how to communicate between nodes with reliable connections and how to dynamically change the job of each node at run-time with little overhead. Second is the development of optimal task assignment and scheduling algorithms. Here, a machine learning approach will be used to generate a recognition model architecture optimal for each distributed system configuration. Third is the development of low-resolution deep neural network (DNN) systems to utilize low-power computing nodes. The development of these DNN systems will involve identifying multiple low-resolution filters that are optimal for varying configurations.
The proposed technical work will advance the state of the art in implementation of parallel and decentralized DNN systems, thereby benefiting all scientific fields of endeavor that rely on computing. The decentralized DNN system will offer new opportunities in power constrained mobile platforms for applications including surveillance and automotive. The research results will lead to new materials/courses for computer architecture and systems. The proposed infrastructure will also be used to guide undergraduate students' research activities.
The software infrastructure will be maintained as an open source project, which can be found at https://github.com/parallel-ml. It will be updated periodically as new outcomes become available. The results will be published in conferences, journals and technical reports.
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.
