MetaTF 2.3.0
Overview
Installation
Requirements
Quick installation
Running examples
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
The Sequential model
Specifying the model
Accessing layer parameters and weights
Inference
Saving and loading
Input layer types
Data-Processing layer types
Model Hardware Mapping
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
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
API reference
Akida runtime
Model
Layer
Layer
Mapping
InputData
InputConvolutional
FullyConnected
Convolutional
SeparableConvolutional
Layer parameters
LayerType
Padding
PoolType
Optimizers
Sequence
Sequence
BackendType
Pass
Device
Device
HwVersion
HWDevice
HWDevice
SocDriver
ClockMode
PowerMeter
NP
Tools
Sparsity
Compatibility
CNN2SNN
Tool functions
quantize
quantize_layer
convert
check_model_compatibility
load_quantized_model
Transforms
Calibration
Quantizers
WeightQuantizer
LinearWeightQuantizer
StdWeightQuantizer
StdPerAxisQuantizer
MaxQuantizer
MaxPerAxisQuantizer
Quantized layers
QuantizedConv2D
QuantizedDense
QuantizedSeparableConv2D
QuantizedActivation
ActivationDiscreteRelu
QuantizedReLU
Akida models
Layer blocks
conv_block
separable_conv_block
dense_block
Helpers
BatchNormalization gamma constraint
Knowledge distillation
Pruning
Training
MACS
Utils
Model zoo
AkidaNet
Mobilenet
DS-CNN
VGG
YOLO
ConvTiny
PointNet++
GXNOR
Examples
General examples
CNN2SNN tutorials
Edge examples
General examples
GXNOR/MNIST inference
AkidaNet/ImageNet inference
DS-CNN/KWS inference
Regression tutorial
Transfer learning with AkidaNet for PlantVillage
YOLO/PASCAL-VOC detection tutorial
CNN2SNN tutorials
CNN conversion flow tutorial
Advanced CNN2SNN tutorial
Edge examples
Akida vision edge learning
Akida edge learning for keyword spotting
Tips to set Akida learning parameters
Model zoo performances
Image domain
Classification
Object detection
Regression
Face recognition
Audio domain
Keyword spotting
Time domain
Fault detection
Classification
Point cloud
Classification
Changelog
Support
License
Akida Examples
»
Akida examples
»
CNN2SNN tutorials
CNN2SNN tutorials
CNN conversion flow tutorial
CNN conversion flow tutorial
Advanced CNN2SNN tutorial
Advanced CNN2SNN tutorial
