Tutorials

We provide for most topics a hands-on guide. If you miss something please create an issue:

• Activation and Quantization Functions Tutorial
• Image Autoencoder Training Tutorial
• Algorithmic Fairness: Invariant Representations via QKeras Bernoulli Activation
• CIFAR-10 Network with Ultra-Low Precision PO2 Quantization
• Quantized CNN
• High/Low Spatial Frequency Decoupling with QOctaveConv2D
• Codebook based quantization
• Divide and Conquer (DnC) Hardware Cost Modeling
• Keras Model Quantization
• QTools: Energy Profiling and Hardware DataType Statistics
• Quantized RNN
• Stochastic Rounding Simulations for Ternary Quantization
• MNIST Dense Multilayer Perceptron (MLP)
• MNIST with Binary Weight Export and Quantized Save Utilities
• MNIST Model with BinaryToThermometer
• MNIST with Power-of-Two (PO2) Quantization
• MNIST with Batch Normalization as a Learned Scale Factor