TinyML Talks Germany - Neural Network Framework Using Emerging Technologies for Screening Diabetic

This technical talk presents an offline diabetic retinopathy screening system using convolutional neural networks on handheld devices. It covers dataset preparation, severity labeling, model training and compression, and implementation on resistive-RAM compute-in-memory architecture with latency and energy comparisons.

Syllabus

Intro
Diabetic Retinopathy(DR)
Convolutional Neural Networks CN
Related Work - CNN Literature
Computation in memory (CIM) Architecture
Resistive Random Access Memory RE
Tasks in this work
Performance Metrics
Data Processing
Model Training
Severity Label (SL)
Model Compression
Multi-class accuracy
Severity Label Accuracy
Model Evaluation
Compression Schemes
Latency
Energy Consumption - Quantization
Conclusion
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