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Akida, 2nd Generation
  • Overview
  • Installation
    • Supported configurations
    • Quick installation
    • Running examples
  • User guide
    • Akida user guide
      • Overview
      • Programming interface
        • The Akida Model
        • Akida layers
      • Model Hardware Mapping
        • Devices
        • Model mapping
        • Advanced Mapping Details and Hardware Devices Usage
        • Performance measurement
      • Using Akida Edge learning
        • Learning constraints
        • Compiling a layer
    • QuantizeML toolkit
      • Overview
      • The FixedPoint representation
      • Quantization flow
        • Compatibility constraints
        • Model loading
      • Command line interface
        • quantize CLI
        • config CLI
        • check CLI
        • insert_rescaling CLI
      • Supported layer types
    • CNN2SNN toolkit
      • Overview
      • Conversion flow
        • Conversion compatibility
        • Command-line interface
      • Handling Akida 1.0 and Akida 2.0 specificities
      • Legacy quantization API
        • Typical quantization scenario
        • Design compatibility constraints
        • Command-line interface
        • Layers Considerations
        • Tips and Tricks
    • Akida models zoo
      • Overview
      • Command-line interface for model creation
      • Command-line interface for model training
        • KWS training
        • AkidaNet training
      • Command-line interface for model evaluation
      • Command-line interface to evaluate model MACS
      • Layer Blocks
      • Handling Akida 1.0 and Akida 2.0 specificities
    • Akida 1.0 Hardware constraints
  • API reference
    • Akida runtime
      • Model
      • Layer
        • Layer
        • Mapping
      • Akida V1 layers
      • Akida V2 layers
      • Layer parameters
        • LayerType
        • Padding
        • PoolType
      • Optimizers
      • Sequence
        • Sequence
        • BackendType
        • Pass
      • Device
        • Device
        • HwVersion
      • HWDevice
        • HWDevice
        • SocDriver
        • ClockMode
      • PowerMeter
      • NP
      • Tools
        • Sparsity
    • CNN2SNN
      • Akida version
      • Conversion
      • Legacy quantization API
        • Utils
        • Calibration
        • Transforms
        • Constraint
        • Quantization
        • Quantizers
        • Quantized layers
    • QuantizeML
      • Layers
        • Reshaping
        • Activations
        • Attention
        • Normalization
        • Convolution
        • Depthwise convolution
        • Separable convolution
        • Dense
        • Skip connection
        • Pooling
        • Shiftmax
        • Transformers
        • Rescaling
        • Dropout
        • Quantizers
        • Quantization parameters
        • Calibration
        • Recording
      • Models
        • Transforms
        • Quantization
        • Calibration
        • Utils
      • Tensors
        • QTensor
        • FixedPoint
        • QFloat
    • Akida models
      • Layer blocks
        • CNN blocks
        • Transformers blocks
        • Transposed blocks
        • Detection block
      • Helpers
        • Gamma constraint
        • Unfusing SeparableConvolutional
        • Extract samples
      • Knowledge distillation
      • MACS
      • Model I/O
      • Utils
      • Model zoo
        • AkidaNet
        • Mobilenet
        • DS-CNN
        • VGG
        • YOLO
        • PointNet++
        • GXNOR
        • CenterNet
        • AkidaUNet
        • Transformers
  • Examples
    • General examples
      • Global Akida workflow
        • 1. Create and train
        • 2. Quantize
        • 3. Convert
        • 4. GXNOR/MNIST
      • AkidaNet/ImageNet inference
        • 1. Dataset preparation
        • 2. Pretrained quantized model
        • 3. Conversion to Akida
        • 4. Hardware mapping and performance
      • 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
        • 4. Conversion to Akida
        • 5. Confusion matrix
      • Age estimation (regression) example
        • 1. Load the UTKFace Dataset
        • 2. Load a pre-trained native Keras model
        • 3. Load a pre-trained quantized Keras model
        • 4. Conversion to Akida
        • 5. Estimate age on a single image
      • Transfer learning with AkidaNet for PlantVillage
        • Transfer learning process
        • 1. Dataset preparation
        • 2. Get a trained AkidaNet base model
        • 3. Add a classification head to the model
        • 4. Freeze the base model
        • 5. Train for a few epochs
        • 6. Quantize the model
        • 7. Compute accuracy
      • YOLO/PASCAL-VOC detection tutorial
        • 1. Introduction
        • 2. Preprocessing tools
        • 3. Model architecture
        • 4. Training
        • 5. Performance
        • 6. Conversion to Akida
      • Segmentation tutorial
        • 1. Load the dataset
        • 2. Load a pre-trained native Keras model
        • 3. Load a pre-trained quantized Keras model
        • 4. Conversion to Akida
        • 5. Segment a single image
      • Build Vision Transformers for Akida
        • 1. Model selection
        • 2. Model optimization for Akida hardware
        • 3. Model Training
        • 4. Model quantization
        • 5. Conversion to Akida
        • 6. Displaying results Attention Maps
    • Quantization
      • Advanced QuantizeML tutorial
        • 1. Defining a quantization scheme
        • 2. Calibration
      • Upgrading to Akida 2.0
        • 1. Workflow differences
        • 2. Models architecture differences
        • 3. Using AkidaVersion
    • Edge examples
      • Akida vision edge learning
        • 1. Dataset preparation
        • 2. Prepare Akida model for learning
        • 3. Edge learning with Akida
      • Akida edge learning for keyword spotting
        • 1. Edge learning process
        • 2. Dataset preparation
        • 3. Prepare Akida model for learning
        • 4. Learn with Akida using the training set
        • 5. Edge learning
      • Tips to set Akida edge learning parameters
        • 1. Akida learning parameters
        • 2. Create Akida model
        • 3. Estimate the required number of weights of the trainable layer
        • 4. Estimate the number of neurons per class
    • [Deprecated] CNN2SNN tutorials
      • Advanced CNN2SNN tutorial
        • 1. Design a CNN2SNN quantized model
        • 2. Weight Quantizer Details
        • 3. Understanding quantized activation
        • 4. How to deal with too high scale factors
  • Model zoo performance
    • Akida 1.0 models
      • Image domain
        • Classification
        • Object detection
        • Regression
        • Face recognition
      • Audio domain
        • Keyword spotting
      • Point cloud
        • Classification
    • Akida 2.0 models
      • Image domain
        • Classification
        • Object detection
        • Regression
        • Face recognition
        • Segmentation
      • Audio domain
        • Keyword spotting
      • Point cloud
        • Classification
  • Changelog
  • Support
  • License
Akida Examples
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