Projects

PI(s): Sarma Vrudhula

Deep neural networks (DNNs) show promise in applications like agriculture, automotive, and construction machinery autonomy. DNNs can adapt to variations in lighting, background clutter, etc., making them robust in real-world scenarios where captured images are of a diverse quality due to different operating and environmental conditions.

from Toward an Enablement of DNN on Edge Devices

PI(s): Siddharth Srivastava

The long-lasting idea of human-robot cooperation has thrived remarkably in the last decade and cobots are being employed increasingly to provide diversified services which has led to more and more human-robot co-working environments.

from Communication Planning for Safe Human-Robot Interaction

PI(s): Sarma Vrudhula

Deep neural networks (DNNs), a prominent framework in machine learning, are characterized by their high computational and energy demands, often involving billions of operations and large working datasets during training.

from 3D Processing-in-Memory Architectures for Learning and Inference

PI(s): Spyros Tragoudas

In Year 1, the efforts in the project focused on developing a deep learning-based approach to identify early delay defects in ICs using responses captured at the internal Test Points (TP). 

from In-Field Circuit Monitoring with Machine Learning

PI(s): Spyros Tragoudas, Haibo Wang, Steven Blair

Point clouds are discrete data points representing 3D shapes and objects in space. Thanks to its ability to accurately describe complex spatial information, point cloud emerged as an attractive technique for various applications, including building modeling, infrastructure mapping, autonomous driving, robotics, virtual reality, etc.

from AI-assisted Characterization and Reduction of Point Cloud Noise