Shreyas Bachiraju

Informatics

Hometown: Bangalore, Karnataka, India

Graduation date: Spring 2026

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FURI | Spring 2025

Compression of Deep Neural Networks for Deployment on Edge Devices

Large deep neural networks (DNNs) are essential for autonomous driving tasks such as object detection, lane tracking, and collision warnings. However, they pose significant deployment challenges on resource-constrained edge devices. This research study investigates how compression algorithms, specifically quantization, can reduce the size of depth models while retaining at least 90% of the original model’s accuracy while increasing real-time inference speed. The NVIDIA Jetson Nano-based Duckiebot DB21 is the testbed for real-time inference. If successful, inference and decision-making can be much quicker than relying on larger external computational resources.

Mentor: Hua Wei

More projects from the current symposium

Pragya Kumari

Computer science

Optimizing Turning Behavior and Lighting Conditions to Reduce Carbon Emissions in Urban Driving

We are investigating various driving behaviors to reduce carbon emissions through turning behavior and lighting conditions.

Mentor: Hua Wei

Program: GCSP research stipend

Henry Alexander Lepp

Electrical engineering

Energy-efficient Wafer Defect Detection Using Spiking Neural Networks

Developing an energy-efficient method for silicon defect detection allows assessment of production techniques in a more sustainable way.

Mentor: Leslie Hwang

Program: FURI

Anthony De Luz

Biomedical engineering

Artificial Intelligent Materials Crystal Classifier ( A.I.- MC2)

Developing custom machine learning algorithms will expedite the current labor intensive x-ray diffraction analyses of materials.

Mentor: Vincent Pizziconi