TinyMaix是面向单片机的超轻量级的神经网络推理库，即TinyML推理库，可以在任意单片机上运行的轻量级深度学习模型。

一、环境准备

编译环境为Ubuntu20.04.

root@EliteDesk:~$ uname -a
Linux EliteDesk 5.15.0-52-generic #58~20.04.1-Ubuntu SMP Thu Oct 13 13:09:46 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux

准备安装好arm-linux-gnueabihf-gcc交叉编译环境，具体过程不再敷述，最终信息如下，SSD201的ARM A7支持硬件浮点，所以选择了gnueabihf。

root@EliteDesk:~$ arm-linux-gnueabihf-gcc -v
Using built-in specs.
COLLECT_GCC=arm-linux-gnueabihf-gcc
COLLECT_LTO_WRAPPER=/usr/lib/gcc-cross/arm-linux-gnueabihf/9/lto-wrapper
Target: arm-linux-gnueabihf
Configured with: ../src/configure -v --with-pkgversion='Ubuntu 9.4.0-1ubuntu1~20.04.1' --with-bugurl=file:///usr/share/doc/gcc-9/README.Bugs --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,gm2 --prefix=/usr --with-gcc-major-version-only --program-suffix=-9 --enable-shared --enable-linker-build-id --libexecdir=/usr/lib --without-included-gettext --enable-threads=posix --libdir=/usr/lib --enable-nls --with-sysroot=/ --enable-clocale=gnu --enable-libstdcxx-debug --enable-libstdcxx-time=yes --with-default-libstdcxx-abi=new --enable-gnu-unique-object --disable-libitm --disable-libquadmath --disable-libquadmath-support --enable-plugin --enable-default-pie --with-system-zlib --without-target-system-zlib --enable-libpth-m2 --enable-multiarch --enable-multilib --disable-sjlj-exceptions --with-arch=armv7-a --with-fpu=vfpv3-d16 --with-float=hard --with-mode=thumb --disable-werror --enable-multilib --enable-checking=release --build=x86_64-linux-gnu --host=x86_64-linux-gnu --target=arm-linux-gnueabihf --program-prefix=arm-linux-gnueabihf- --includedir=/usr/arm-linux-gnueabihf/include
Thread model: posix
gcc version 9.4.0 (Ubuntu 9.4.0-1ubuntu1~20.04.1)

二、源码下载和编译

直接git clone源码。

https://github.com/sipeed/TinyMaix.git

然后安装如下步骤编译：

cd examples/mnist
mkdir build
cd build
cmake ..
make
./mnist

最终得到可执行文件，相关信息如下。

root@EliteDesk:~$ file mnist
mnist: ELF 32-bit LSB shared object, ARM, EABI5 version 1 (SYSV), dynamically linked, interpreter /lib/ld-linux-armhf.so.3, BuildID[sha1]=f53f0191eea5817b6bead1e420d1fc4e4c9f2bcb, for GNU/Linux 3.2.0, not stripped

三、运行demo

将编译生成的可执行文件传输到Purple Pi开发板，并且执行。

# ./mnist
mnist demo
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  0,  0,  0,  0,  0,  0,  0, 52,250,253,210, 32, 12,  0,  6,206,253,140,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
  0,  0,  0,  0,  0,  0,  0, 77,251,210, 25,  0,  0,  0,122,248,253, 65,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
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================================ model stat ================================
mdl_type=0 (int8))
out_deq=1
input_cnt=1, output_cnt=1, layer_cnt=6
input 3dims: (28, 28, 1)
output 1dims: (1, 1, 10)
main buf size 1464; sub buf size 0

===tm_run use 0.244 ms
0: 0.004
1: 0.004
2: 0.996
3: 0.004
4: 0.000
5: 0.000
6: 0.004
7: 0.004
8: 0.004
9: 0.004
### Predict output is: Number 2, prob 0.996

use 0.244 ms，可以对比下已有板子的benchmark，比ARM A53的0.14ms要慢点，比RV64V（D1-H）的0.29ms又要快点。

##感谢阅读##