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Akida 1.8.9
  • Overview
  • Installation
    • Requirements
    • Quick installation
    • Running examples
  • User guide
    • Getting started
      • For beginners
      • For users familiar with deep-learning
    • Akida user guide
      • The Akida Execution Engine
        • 1. The Spiking Neural Network model
        • 2. Input data format
        • 3. Determine training mode
        • 4. Interpreting outputs
      • Neural Network model
        • Specifying the Neural Network model
        • Saving and loading
        • Input layer types
        • Data-Processing layer types
      • Using Akida Unsupervised Learning
        • Learning constraints
        • Compiling a layer
        • Learning parameters
    • 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
        • Final Layers
      • Tips and Tricks
    • Akida models zoo
      • Overview
      • Command-line interface for model creation
      • Command-line interface for model training
        • CIFAR10 training and tuning
        • UTK Face training
        • KWS training
        • YOLO training
      • Layer Blocks
        • conv_block
        • dense_block
        • separable_conv_block
    • Hardware constraints
      • Input layer
      • Data-Processing layers
        • Convolutional layer
        • Fully connected layer
  • API reference
    • Akida Execution Engine
      • Model
      • Layer
      • LayerStatistics
      • Observer
      • InputData
      • InputConvolutional
      • FullyConnected
      • Convolutional
      • SeparableConvolutional
      • Dense
      • Sparse
      • coords_to_sparse
      • dense_to_sparse
      • packetize
      • Backend
      • ConvolutionMode
      • PoolingType
      • LearningType
      • Compatibility
    • CNN2SNN
      • quantize
      • quantize_layer
      • convert
      • check_model_compatibility
      • WeightQuantizer
      • TrainableWeightQuantizer
      • MaxQuantizer
      • MaxPerAxisQuantizer
      • WeightFloat
      • QuantizedConv2D
      • QuantizedDepthwiseConv2D
      • QuantizedDense
      • QuantizedSeparableConv2D
      • ActivationDiscreteRelu
      • QuantizedReLU
    • Akida models
      • Quantization blocks
        • conv_block
        • separable_conv_block
        • dense_block
      • Layer blocks
        • conv_block
        • separable_conv_block
        • dense_block
      • Model zoo
        • Mobilenet
        • DS-CNN
        • VGG
        • YOLO
  • Examples
    • GXNOR/MNIST inference
      • 1. Dataset preparation
      • 2. Load the pre-trained Akida model
      • 3. Show predictions for a single image
      • 4. Check performance
    • Regression tutorial
      • 1. Load the dataset
      • 2. Load a pre-trained native Keras model
      • 3. Load a pre-trained quantized Keras model satisfying Akida NSoC requirements
      • 4. Conversion to Akida
      • 5. Estimate age on a single image
    • CNN conversion flow tutorial
      • 1. Load and reshape MNIST dataset
      • 2. Model definition
        • 3. Model training
      • 4. Model quantization
      • 5. Model fine tuning (quantization-aware training)
      • 6. Model conversion
    • DS-CNN/KWS inference
      • 1. Load the preprocessed dataset
      • 2. Load a pre-trained native Keras model
      • 3. Load a pre-trained quantized Keras model satisfying Akida NSoC requirements
      • 4. Conversion to Akida
        • 5. Confusion matrix
    • DS-CNN CIFAR10 inference
      • 1. Dataset preparation
      • 2. Create a Keras DS-CNN model
      • 3. Quantized model
      • 4. Pretrained quantized model
      • 5. Conversion to Akida
        • 5.1 Convert to Akida model
        • 5.2 Check hardware compliancy
        • 5.3 Check performance
        • 5.4 Show predictions for a random image
    • YOLO/PASCAL-VOC detection tutorial
      • 1. Introduction
        • 1.1 Object detection
        • 1.2 YOLO key concepts
      • 2. Preprocessing tools
      • 3. Model architecture
      • 4. Training
      • 5. Performance
      • 6. Conversion to Akida
        • 6.1 Convert to Akida model
        • 6.1 Check performance
        • 6.2 Show predictions for a random image
    • Transfer learning with MobileNet for cats vs. dogs
      • Transfer learning process
      • 1. Load and preprocess data
        • 1.A - Load and split data
        • 1.B - Preprocess the test set
        • 1.C - Get labels
      • 2. Modify a pre-trained base Keras model
        • 2.A - Instantiate a Keras base model
        • 2.B - Modify the network for the new task
      • 3. Train the transferred model for the new task
      • 4 Quantize the top layer
      • 5. Convert to Akida
      • 6. Plot confusion matrix
    • MobileNet/ImageNet inference
      • 1. Dataset preparation
      • 2. Create a Keras MobileNet model
      • 3. Quantized model
      • 4. Pretrained quantized model
      • 5. Conversion to Akida
        • 5.1 Convert to Akida model
        • 5.2 Check hardware compliancy
        • 5.3 Check performance
        • 5.4 Show predictions for a random image
  • Changelog
  • Support
  • License
Akida Examples
  • »
  • User guide

User guideΒΆ

  • Getting started
    • For beginners
    • For users familiar with deep-learning
  • Akida user guide
    • The Akida Execution Engine
      • 1. The Spiking Neural Network model
      • 2. Input data format
        • Data format
        • Input events buffering
        • Data frames parallelization
      • 3. Determine training mode
        • Akida Unsupervised learning
        • CNN to SNN conversion
      • 4. Interpreting outputs
    • Neural Network model
      • Specifying the Neural Network model
      • Saving and loading
      • Input layer types
      • Data-Processing layer types
        • Activation parameters
        • Pooling parameters
    • Using Akida Unsupervised Learning
      • Learning constraints
      • Compiling a layer
      • Learning parameters
  • 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
      • CIFAR10 training and tuning
      • UTK Face training
      • KWS training
      • YOLO training
    • Layer Blocks
      • conv_block
      • dense_block
      • separable_conv_block
  • Hardware constraints
    • Input layer
    • Data-Processing layers
      • Convolutional layer
      • Fully connected layer
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