This app provides benchmarks and tests for a number of common deep learning
network operations:

- AveragePool
- Convolution
- DepthwiseConvolution
- Im2col
- MatrixMultiply
- MaxPool

The benchmarks are set up to measure the performance of these operations as used
in an open-sourced MobileNet v1 model.

This app is intended to provide fast implementations of common deep learning
network operations on all platforms that Halide supports.

These operations were developed at Google and contributed by David Chen, Ronald
Wotzlaw, Huizhong Chen, and Volodymyr Kysenko (@vksnk on GitHub).

# TODOs and notes

- Support data types other than 8-bit integers

- Optimize Hexagon schedules (some room for improvement)

- Optimize CPU schedules (much room for improvement)

- Implement GPU schedules

- Im2Col is used with MatrixMultiply to implement Convolution. This approach
  makes sense when one has access to a good matrix multiply implementation to
  use and cannot be modified easily. However, a good schedule for Convolution
  should be at least as good as Im2Col + MatrixMultiply (at worst, the two
  stages can be in the same Halide pipeline, with Im2Col compute_root).
  Therefore, it might make sense to spend effort optimizing Convolution rather
  than Im2Col.

# Build and test

To build and run these benchmarks and tests:

    HL_TARGET=host make run

To build and run these benchmarks and tests on an ARM Android device:

    HL_TARGET=arm-64-android ./adb_run_on_device.sh