Cooperative AI Inference
NSF Award #2128341
Start Date: October 1, 2021
End Date: September 30, 2024 (Estimated)
Former PI: Dr. Ning Wang
Current PI: Dr. Shen-Shyang Ho
Abstract
Artificial Intelligence (AI) has shown superior performance in enhancing driving safety in advanced driver-assistance systems (ADAS). State-of-the-art deep neural networks (DNNs) achieve high accuracy at the expense of increased model complexity, which raises the computation burden of onboard processing units of vehicles for ADAS inference tasks. The primary goal of this project is to develop innovative collaborative AI inference strategies with the emerging edge computing paradigm. The strategies can adaptively adjust cooperative inference techniques for best utilizing available computation and communication resources and ultimately enable high-accuracy and real-time inference. The project will inspire greater collaborations between experts in wireless communication, edge computing, computer vision, autonomous driving testbed development, and automotive manufacturing, and facilitate AI applications in a variety of IoT systems. The educational testbed developed from this project can be integrated into courses to provide hands-on experiences. This project will benefit undergraduate, master, and Ph.D. programs and increase under-represented groups' engagement by leveraging the existing diversity-related outreach efforts.
A multi-disciplinary team with complementary expertise from Rowan University, Temple University, Stony Brook University, and Kettering University is assembled to pursue a coordinated study of collaborative AI inference. The PIs explore integrative research to enable deep learning technologies in resource-constrained ADAS for high-accuracy and real-time inference. Theory-wise, the PIs plan to take advantage of the observation that DNNs can be decomposed into a set of fine-grained components to allow distributed AI inference on both the vehicle and edge server sides for inference acceleration. Application-wise, the PIs plan to design novel DNN models which are optimized for the cooperative AI inference paradigm. Testbed-wise, a vehicle edge computing platform with V2X communication and edge computing capability will be developed at Kettering University GM Mobility Research Center. The cooperative AI inference system will be implemented, and the research findings will be validated on realistic vehicular edge computing environments thoroughly. The data, software, and educational testbeds developed from this project will be widely disseminated. Domain experts in autonomous driving testbed development, intelligent transportation systems, and automotive manufacturing will be engaged in project-related issues to ensure relevant challenges in this project are impactful for real-world applications.
Participating Graduate/Undergraduate Students:
Former:
Asmika Boosarapu (10/1/2021-5/31/2023) MSCS (Thesis)
Jack Campanella - BS ECE
Matthew McBurney - BS ECE
Sarah Ely - BS ECE
Lauren Eckert - BS ECE
Current:
Nicholas Bovee - MSCS
Stephen Piccolo - BS CS
Richard Brown - BS ME
Gopi Krishna Patapanchala - MSDS
Suraj Bitla - MSCS
Publications (Rowan):
Nicholas Bovee, Stephen Piccolo, Shen Shyang Ho, and Ning Wang Experimental test-bed for Computation Offloading for Cooperative Inference on Edge Devices, EdgeComm: The Fourth Work-shop on Edge Computing and Communications (at ACM/IEEE Symposium on Edge Computing), December 9, 2023, Wilmington, DE
Purva Makarand Mhasakar, Kevin Doshi, Ning Wang, Shen Shyang Ho, and Haibin Ling, Distributed Tracking and Verifying: A Real-Time and High-Accuracy Visual Tracking Edge Computing Framework for Internet of Things, EdgeComm: The Fourth Workshop on Edge Computing and Communications (at ACM/IEEE Symposium on Edge Computing), December 9, 2023, Wilmington, DE
Shen-Shyang Ho, Paolo Rommel Sanchez, Nicholas Bovee, Suraj Bitla, Gopi Krishna Patapanchala and Stephen Piccolo, Computation Offloading for Precision Agriculture using Cooperative Inference, 8th IEEE International Conference on Fog and Edge Computing (ICFEC 2024), Philadelphia, PA, May 6-9, 2024
Nicholas Bovee, Stephen Piccolo#, Suraj Bitla, Gopi Krishna Patapanchala and Shen-Shyang Ho, SplitTracer: A Cooperative Inference Evaluation Toolkit for Computation Offloading on the Edge, 8th IEEE International Conference on Fog and Edge Computing (ICFEC 2024), Philadelphia, PA, May 6-9, 2024
Other Related Publications (Not funded by this project):
Lahari Soumya Voleti and Shen-Shyang Ho, Personalized Learning with Limited Data on Edge Devices using Federated Learning and Meta-Learning, EdgeComm: The Fourth Workshop on Edge Computing and Communications (at ACM/IEEE Symposium on Edge Computing), December 9, 2023, Wilmington, DE