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An optimized neural network operator library for chips base on Xuantie CPU.

License: Apache License 2.0

C 78.39% Assembly 2.16% Makefile 0.08% Python 8.84% Shell 0.10% CMake 1.76% C++ 8.65% GDB 0.01%
deep-learning inference-engine neural-network risc-v riscv riscv-assembly

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csi-nn2's Issues

Cannot build c908

Toolchain: Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.6.1-20220906.tar.gz

Reproduce:

make nn2_c908

got

/mnt/home-nas/work/repo/csi-nn2/source/thead_rvv/data_convert.c: In function 'shl_rvv_data_convert_int8_to_int4':
/mnt/home-nas/work/repo/csi-nn2/source/thead_rvv/data_convert.c:48:26: error: implicit declaration of function 'vpnclip_wx_i8m1'; did you mean 'vnclip_wx_i8m1'? [-Werror=implicit-function-declaration]
   48 |         vint8m1_t _res = vpnclip_wx_i8m1(vreinterpret_v_i8m2_i16m2(_tmp), 0, vl / 2);
      |                          ^~~~~~~~~~~~~~~
      |                          vnclip_wx_i8m1
/mnt/home-nas/work/repo/csi-nn2/source/thead_rvv/data_convert.c:48:26: error: incompatible types when initializing type 'vint8m1_t' using type 'int'
/mnt/home-nas/work/repo/csi-nn2/source/thead_rvv/data_convert.c: In function 'shl_rvv_data_convert_int4_to_int8':
/mnt/home-nas/work/repo/csi-nn2/source/thead_rvv/data_convert.c:71:27: error: implicit declaration of function 'vpwadd_vx_i16m2'; did you mean 'vwadd_vx_i16m2'? [-Werror=implicit-function-declaration]
   71 |         vint16m2_t _tmp = vpwadd_vx_i16m2(_input, 0, vl);
      |                           ^~~~~~~~~~~~~~~
      |                           vwadd_vx_i16m2
/mnt/home-nas/work/repo/csi-nn2/source/thead_rvv/data_convert.c:71:27: error: incompatible types when initializing type 'vint16m2_t' using type 'int'
cc1: all warnings being treated as errors
make[2]: *** [CMakeFiles/c908_static.dir/build.make:7454: CMakeFiles/c908_static.dir/source/thead_rvv/data_convert.c.o] Error 1
make[1]: *** [CMakeFiles/Makefile2:83: CMakeFiles/c908_static.dir/all] Error 2
make: *** [Makefile:136: all] Error 2

例子

  1. 大哥能给个怎么用的例子吗?test里面是有例子,但是makefile文件没有公开,哈哈。(虽然可以自己写)
  2. 比如能不能用CSI-NN2完成一个Lenet5的例子 。

非常感谢,这个库对我们很有帮助,我们打算移植到其他硬件平台上,谢谢!

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