RFSoC Modulation Classification With Streaming CNN: Data Set Generation & Quantized-Aware Training
This paper introduces a novel FPGA-based Convolutional Neural Network (CNN) architecture for continuous radio data processing, specifically targeting modulation classification on the Zynq UltraScale+ Radio Frequency System on Chip (RFSoC) operating in real-time. Evaluated on AMD’s RFSoC2x...
Saved in:
Main Authors: | Andrew Maclellan, Louise H. Crockett, Robert W. Stewart |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Open Journal of Circuits and Systems |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10772713/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A comprehensive review on early detection of drusen patterns in age-related macular degeneration using deep learning models
by: Kiruthika M, et al.
Published: (2025-02-01) -
Dry and neovascular “wet” age-related macular degeneration: Upcoming therapies
by: Audrey Yan, et al.
Published: (2025-01-01) -
Explainable AI-Based Approach for Age-Related Macular Degeneration (AMD) Detection via Fundus Imaging
by: Ainhoa Osa-Sanchez, et al.
Published: (2025-01-01) -
FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs
by: Mustafa Tasci, et al.
Published: (2025-01-01) -
Breast cancer classification based on hybrid CNN with LSTM model
by: Mourad Kaddes, et al.
Published: (2025-02-01)