8. Advanced Features

Tiny ML Tensorlab includes several advanced features to help you build more accurate and efficient models. This section covers these capabilities in detail.

Contents

8.7. Feature Overview

Neural Architecture Search (NAS)

Automatically discover optimal neural network architectures for your dataset. NAS can optimize for memory usage or computational efficiency.

  • Preset sizes: s, m, l, xl, xxl

  • Optimization modes: Memory or Compute

  • GPU recommended for practical use

Quantization

Reduce model size and improve inference speed through quantization:

  • QAT (Quantization-Aware Training) - Best accuracy

  • PTQ (Post-Training Quantization) - Faster, no retraining

  • Weight bit-widths: 2-bit, 4-bit, 8-bit

Standalone Quantization Examples

Runnable Python examples demonstrating direct use of quantization wrappers:

  • FMNIST, Audio KWS, Motor Fault, MNIST, Torque Regression

  • QAT and PTQ workflows with 2/4/8-bit quantization

  • ONNX export and inference validation

Feature Extraction

Transform raw time-series data into meaningful features:

  • FFT (Fast Fourier Transform)

  • Binning and normalization

  • Haar and Hadamard wavelets

  • Logarithmic scaling

Goodness of Fit Test

Evaluate whether your dataset is suitable for classification before training. Uses PCA and t-SNE visualization to assess class separability.

Post-Training Analysis

Understand model performance with:

  • ROC curves for classification

  • Confusion matrices

  • FPR/TPR threshold analysis

  • PCA visualization of feature-extracted data