SBIR Phase I: A Hardware-Aware AutoML Platform for Resource-Constrained Devices

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to automate manufacturing inspection, reducing the cost of certain highly engineered projects. This project will develop a system to automatically create machine learning models for resource-constrained industrial hardware. This project enables the fast, affordable, and easy creation of on-device artificial intelligence with significant industrial applications. This will improve robust vision-inspection tools to improve product quality.

This project will develop an efficient solution to create advanced industrial internet of things applications that reduce network stress, minimize latency, and increase security at the edge. While the internet of things has many applications in the manufacturing industry, the vision inspection market can benefit due to its low-latency needs and the high stress it produces in the supporting infrastructure.

This project is a flexible and modular hardware-aware machine learning model generation system that reduces manual efforts by automatically generating complex machine learning models for resource-constrained devices. Technical hurdles include the generation of neural networks that consider hardware speed and capacity constraints without human intervention.

Technical milestones involve creating an automated model discovery platform tailored for edge devices, new resource-constrained neural architecture search algorithms, and hardware-aware model compression. This research aims to produce a prototype that domain experts with limited computer science training can use to create advanced industrial internet of things solutions.

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.