CAREER: Adaptive Deep Learning Systems Towards Edge Intelligence

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

Edge intelligence pushes intelligent data processing using deep neural networks (DNNs) to the edge of the network, closer to data sources. It enables applications across various fields and has garnered significant attention from both industry and academia. However, the limited resources on edge platforms, such as edge servers and Internet of Things devices, hinder the ability to deliver fast and accurate responses to queries from deep learning prediction tasks. As a result, only some deep learning tasks and smaller DNN models suitable for edge deployment are feasible. To overcome this limitation, this project explores a new adaptive approach in building deep learning systems. The systems will make real-time adjustments to the DNNs executed for prediction tasks based on the varying resource demands arising from three critical dimensions -- variable task complexity, fluctuating inference workloads, and resource contention in multi-tenant edge environments. The goal is to optimize both system efficiency and accuracy. Realizing the envisioned adaptiveness will facilitate the effective deployment of deep learning techniques across diverse applications and environments. This research has the potential to open new possibilities for the development of novel edge applications that were previously limited by resource constraints. It will enable a broader range of deep learning tasks to be executed on edge platforms, along with more powerful DNN models, a capability critical in fully unleashing the potential of edge intelligence. The practical impact of the project will be demonstrated in a variety of applications, and in particular, applications that enhance elder care through collaboration with the Massachusetts AI and Technology Center for Connected Care in Aging and Alzheimer’s Disease. Moreover, this project aims to cultivate a pipeline of skilled engineers and computer scientists with interdisciplinary expertise in computer systems and machine learning. Efforts will be made to recruit underrepresented students through diversity programs and outreach initiatives to K-12 students. 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.