Final Exam: Pranav S Murali

Wednesday 29 May 2024, 12:30 → 13:30 America/Los_Angeles

ECE-M306 (University of Washington)

Scott Hauck, Shih-Chieh Hsu (University of Washington Seattle (US))

Zoom: https://washington.zoom.us/j/95238930427

12:30 → 13:30 Accelerating Electron Diffraction Analysis with Machine Learning Inference on FPGAs.

Abstract:

This thesis presents a novel approach to enhance the analysis of crystal structures using Reflection High-Energy Electron Diffraction (RHEED) in material science. Addressing the current bottleneck of lengthy analysis times in existing RHEED systems, this study introduces a high-speed camera-based system design capable of real-time crystal analysis.

The proposed system leverages a LeNet5-based neural network deployed on an FPGA-based framegrabber to accelerate image processing. Through meticulous optimization efforts including neural network quantization and Vivado optimization directives, the system achieves latencies of 450-750 us, facilitating RHEED image analysis at speeds of up to 1000 Hz. This advancement promises researchers real-time insights into crystal growth processes, empowering timely interventions for desired experimental outcomes.

Speaker: Pranav Srinivas Murali (University of Washington (US))