User guide

• Getting started
  • For beginners
  • For users familiar with deep-learning
• Akida user guide
  • Introduction
    • Akida layers
    • Input Format
    • A versatile machine learning framework
      • Native Spiking Neural Networks
      • Deep-learning Spiking Neural Networks
  • The Sequential model
    • Specifying the model
    • Accessing layer parameters and weights
    • Inference
    • Saving and loading
    • Input layer types
    • Data-Processing layer types
      • Activation parameters
      • Pooling parameters
  • Model Hardware Mapping
    • Devices
      • Discovering Hardware Devices
      • Virtual Devices
    • Model mapping
    • Advanced Mapping Details and Hardware Devices Usage
    • Performances measurement
  • Using Akida Edge learning
    • Learning constraints
    • Compiling a layer
• CNN2SNN toolkit
  • Overview
    • Conversion workflow
    • Typical training scenario
    • Design compatibility constraints
    • Quantization compatibility constraints
    • Command-line interface
  • Layers Considerations
    • Supported layer types
    • CNN2SNN Quantization-aware layers
    • Training-Only Layers
    • First Layers
      • Input Scaling
    • Final Layers
  • Tips and Tricks
• Akida models zoo
  • Overview
  • Command-line interface for model creation
  • Command-line interface for model training
    • UTK Face training
    • KWS training
    • YOLO training
    • AkidaNet training
  • Command-line interface for model evaluation
  • Command-line interface to evaluate model MACS
  • Layer Blocks
    • conv_block
    • dense_block
    • separable_conv_block
• Hardware constraints
  • InputConvolutional
  • Convolutional
  • SeparableConvolutional
  • FullyConnected
• Akida versions compatibility
  • Upgrading models with legacy quantizers