README.md

CompiledNN: A JIT Compiler for Neural Network Inference

Features

• compiles Keras HDF5 models into machine code
• generates single-threaded code for x86/64 processors with SSSE3/SSE4

Dependencies

• HDF5 (C bindings only)

Compiling

CompiledNN can be compiled into a library via CMake:

mkdir build
cd build
cmake ..
make
make install

Another way to integrate CompiledNN is to add it (and its dependency AsmJit) as source files to your project.

Supported layers

• Core
  • Dense
  • Activation
    • relu
    • tanh (approximated)
    • sigmoid (approximated)
    • hard_sigmoid
    • linear
    • softmax (approximated)
    • elu (approximated)
    • selu (approximated)
    • softsign
  • Dropout
  • Flatten
  • Reshape (does not support dimension inference, i.e. specifying -1 as dimension is not allowed)
• Convolutional
  • Conv2D (only with dilation_rate=1)
  • SeparableConv2D (only with dilation_rate=1 and depth_multiplier=1)
  • DepthwiseConv2D (only with dilation_rate=1, depth_multiplier=1, use_bias=False and activation=None)
  • Cropping2D
  • UpSampling2D (only with interpolation=nearest, number of channels must be at most 32/64 and divisible by 4)
  • ZeroPadding2D (number of channels per row must be divisible by 4)
• Pooling
  • MaxPooling2D
  • AveragePooling2D
  • GlobalMaxPooling2D (at most 28/60 channels)
  • GlobalAveragePooling2D (at most 28/60 channels)
• Merge
  • Add
  • Subtract
  • Multiply
  • Average
  • Maximum
  • Minimum
  • Concatenate (number of channels after the concatenation axis must be divisible by 4 for each input)
• Advanced Activations
  • LeakyReLU
  • ELU
  • ThresholdedReLU
  • Softmax (only for flat tensors)
  • ReLU
• Normalization
  • BatchNormalization (only for flat tensors or channel dimension)

Example

#include <CompiledNN/Model.h>
#include <CompiledNN/CompiledNN.h>

using namespace NeuralNetwork;

int main()
{
  Model model;
  model.load("model.h5");
  // Optionally, indicate which input tensors should be converted from unsigned chars to floats in the beginning.
  // model.setInputUInt8(0);
  CompiledNN nn;
  nn.compile(model);
  // ... fill nn.input(i) with data
  nn.apply();
  // ... obtain the results from nn.output(i)
  return 0;
}