Domestic Core Running Tinymaxi's Lightweight Neural Network Reasoning Library-Mill Mill Is Based On Core Chi D9 Domestic Business Board

This article will introduce Mill Electronics MyD-YD9360 Shangxian Board (Mill is based on Xinchi D9360Tinymaxi lightweight neural network reasoning solution test.

Computing power test

TinyMaix It is an ultra -lightweight neural network inference library for the single -chip microcomputer, that isTo let you run a lightweight deep learning model on any single -chip machine ~

The constructed environment is the compiled Ubuntu18.04 that has been pre -installed with the CMAKE MAKE tool. Due to the magic network, download the TAR package to the host in advance, and then transmit it to the card to decompress it in the card card to decompress it.

• View cmake version

cmake -version

• View Make version

make -version

Confirm the file path, try not to copy it under the right path

Comes with examples

File structure

MNISTExemplary example

MNISTIt's handwritten digital recognition task

cdUse mkdir build & amp; & amp; cd built command to switch to the built folder

cmake ..make./ mnist

cmakeGenerate construction system

Use make to build executable files and run

You can see the output information

MNIST Examples did not use any instructions to accelerate by default. A 28 × 28 handwritten digital simulation image was run, and a total of 0.114 milliseconds were consumed.

MBNETExemplary example

mbnet It is a simple image classification model for mobile devices.

Switch to /examples /mbnet directory:

Modify the main.c file

Create a built folder and switch

Use the Cmake command to generate the construction system

Use the make command to build a system to generate executable files

Run executable file, the execution effect is as follows

MBNET Example runs an RGB image of 96 × 96 × 3, output 1000 categories, and consumes a total of 16.615 milliseconds

Run CIFAR10 DEMO

Mill Electron

Source: Mill Electronics

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