Skip to content

Latest commit

 

History

History
89 lines (63 loc) · 2.54 KB

README.md

File metadata and controls

89 lines (63 loc) · 2.54 KB
Raw

Standard APIs for AI operations

Environment Requirement

Dependency Version required
gcc 5.0 or higher
CMake 3.11 or higher

Setup

You can setup Standard APIs for AI operations by following instructions:

  1. Use git clone instruction to download source code

    git clone https://github.com/PKUHPC/aitisa_api_sample.git

  2. Make a new directory build under the project directory, then use cmake instruction

    mkdir build  
cd build  
cmake ..

  3. Use make instruction to compile the code

    make

  4. Run testing file. Take running convolution operator testing file as an example

    cd bin
./conv2d_simple_test

Operator performance optimization

Here are three commonly used operators in the field of deep learning, and what you need to do is optimize them to improve their performance.

matmul

Calculate the result of multiplying two 2D matrices.

You can modify the macro MM_KERNEL_SIMPLE in /src/math/matmul_simple.c to optimize the operator.

If you want to test the operator yourself, you can run this

cd build
cmake ..
make
cd bin
./matmul_simple_test ../../test/test_data/matmul/2_case_32_1024_32_float/input1.dat ../../test/test_data/matmul/2_case_32_1024_32_float/input2.dat ../../test/test_data/matmul/2_case_32_1024_32_float/output.dat

conv2d

Calculate two-dimensional convolution based on 4D input tensor(N, C_in, H, W) and 4D filter(C_in, C_out, K, K).

You can modify the macro CONV2D_KERNEL_SIMPLE in /src/nn/conv2d_simple.c to optimize the operator.

If you want to test the operator yourself, you can run this

cd build
cmake ..
make
cd bin
./conv2d_simple_test ../../test/test_data/conv2d/1_case_2_2_16_16_float/input.dat  ../../test/test_data/conv2d/1_case_2_2_16_16_float/filter.dat ../../test/test_data/conv2d/1_case_2_2_16_16_float/output.dat

resize2d_bilinear

Resize 2D images with bilinear interpolation.

You can modify the macro resize2d_bilinear_kernel in /src/nn/resize2d_bilinear.c to optimize the operator.

If you want to test the operator yourself, you can run this

cd build
cmake ..
make
cd bin
./resize2d_bilinear_test ../../test/test_data/resize2d_bilinear/1_case_1024_1024_512_512_float/input.dat ../../test/test_data/resize2d_bilinear/1_case_1024_1024_512_512_float/output.dat  ../../test/test_data/resize2d_bilinear/1_case_1024_1024_512_512_float/shape.dat