Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 

CompiledNN: A JIT Compiler for Neural Network Inference

Build Status

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
    • 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)
    • 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
  • Advanced Activations
    • LeakyReLU
    • ELU
    • ThresholdedReLU
    • Softmax
    • ReLU
  • Normalization
    • BatchNormalization

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;
}

About

A JIT Compiler for Neural Network Inference

Resources

License

Releases

No releases published

Packages

No packages published