This library designed to perform matrix multiplication on a Neural Processing Unit (NPU) using the Rockchip RKNN toolkit2. licensed under the BSD 3-Clause License.

To install the dependencies of this project, please use the install.sh file. If you want to use the opencv and or python features, add the --opencv=true and or --python=true accordingly.

#include "matrix_types/matrix.hpp"
#include "matrix_types/opencv_mat.hpp"
#include <iostream>
#include <vector>

int main() {

    int rows = 4096;
    int cols = 4096;

    std::vector<int8_t> a(rows*cols, 2);

    std::vector<int8_t> b(rows*cols, 3);

    // without opencv
    Matrix<int8_t> A(rows, cols, a.data());
    Matrix<int8_t> B(rows, cols, b.data());

    Matrix<int32_t> C = A.matmul<int32_t>(B);

    std::cout << "The first item of the matrix: ";
    std::cout << C.data[0] << "\n";

    //with opencv
    MatNpu A(rows, cols, CV_8S, a.data());
    MatNpu B(rows, cols, CV_8S, b.data());

    MatNpu C = A.matmul(B, CV_32S);

    std::cout << "The first item of the matrix: ";
    std::cout << C.at<int32_t>(0, 0) << "\n";
}

import matnpu
import numpy as np

# Generate some random data
a = np.random.randint(low=-128, high=127, size=(320, 320), dtype=np.int8)
b = np.random.randint(low=-128, high=127, size=(320, 320), dtype=np.int8)

# npu calculation
npu_matmul = matnpu.matmul_i32(a, b)
print(npu_matmul)

Also see the example.cpp & example_opencv.cpp files which can be compiled using the Makefile in the repo. or the example.py for python

This library provides a convenient interface for setting up, executing, and managing matrix multiplication operations on NPUs, supporting various data types such as float16, and int8_t as input matrices and float16, int8, and float32 as output matrices.

• Supports multiple data types for input and output matrices.
• Simplifies NPU memory management.
• Provides utility functions to set matrix data and free resources.
• Performs efficient matrix multiplication on NPUs.
• Extention of the OpenCV Mat
• Python bindings

• More operations on the NPU. (Dot Product, convolution, etc..)
• Rust Bindings.