Welcome to BinaryBrain’s documentation!

Contents:

• Introduction
  • Abstruct
  • Quick Start (C++)
    • Windows
    • Linux(Ubuntu 18.04.1)
      • 1. install tools
      • 2. build and run
    • Google Colaboratory
  • Quick Start (Python)
    • install with pip
    • install with setup.py
      • preparation
      • install
  • About github
  • Basic usage
• 事例紹介
  • リアルタイム認識
    • 実装事例
    • FPGAリソース
  • Autoencoder
    • MNIST
    • CIFAR-10
• Evaluation for FPGA
  • sample program
• C++ API
  • Abstruct
  • Model class
    • Basic class
      • Model class
    • Activation layers
      • Binarize class
      • ReLU class
      • Sigmoid class
    • Operation layers
      • SparseLutN class
      • StochasticLutN class
      • MicroMlp class
      • MicroMlpAffine class
      • DenseAffine class
      • BatchNormalization class
      • MaxPooling class
      • LutLayer class
      • BinaryLutN class
    • Utility classes
      • Sequential class
      • LoweringConvolution class
      • ConvolutionIm2 class
      • ConvolutionCol2Im class
      • BinaryModulation class
      • RealToBinary class
      • BinaryToReal class
  • Other classes
    • Loss function classes
      • LossSoftmaxCrossEntropy class
      • LossMeanSquaredError class
    • Metrics classes
      • MetricsCategoricalAccuracy class
      • MetricsMeanSquaredError class
    • Optimizer
      • OptimizerSgd class
      • OptimizerAdam class
    • Assist for run
      • Runner class
    • Containers
      • Tensor class
      • Variables class
      • FrameBuffer class
  • functions
    • export RTL for FPGA
      • ExportVerilog_LutLayers function
      • ExportVerilog_LutCnnLayersAxi4s function
• Python API
  • binarybrain module
• LUT-Networkとは
  • 概要
  • Sparse-LUTモデル
    • Stochastic-LUTモデル
    • Sparse-LUTモデルの全体像
    • Sparse-LUTモデルによるFPGA化
• バイナリ変調
  • 概要
  • 従来のバイナリネットワーク
  • バイナリ変調

Indices and tables

• Index
• Module Index
• Search Page