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Colorado School of Mines, SEAKR Engineering

AI for RF Signal Classification

Colorado School of Mines, SEAKR Engineering

Golden, CO ยท September 2023 - May 2024

Developed Temporal Convolutional Networks (TCNs) for edge computing on AMD Versal FPGA

Deep Learning Edge Computing FPGA PyTorch Signal Processing TCN

Research Overview

Developed AI models for real-time RF signal classification on edge devices, focusing on efficient deployment of deep learning models on FPGA hardware.

Technical Work

Temporal Convolutional Networks (TCNs)

  • Built TCN architectures using PyTorch for RF signal classification
  • Deployed on AMD Versal VCK190 FPGA
  • Leveraged model quantization for efficient inference
  • Integrated AI/DSP engines to expedite model inference in edge computing

Edge Computing Optimization

Focused on making deep learning models viable for edge deployment:

  • Model compression and quantization
  • Hardware-aware optimization
  • Real-time inference on resource-constrained devices

Leadership

Scrum Lead for 8-member Agile team:

  • Facilitated daily stand-ups and sprint planning
  • Coordinated with product owner on requirements
  • Managed project timeline and deliverables

Impact

Demonstrated that state-of-the-art deep learning models can be deployed on edge devices for real-time signal processing, enabling autonomous decision-making in bandwidth-constrained environments like satellite systems.