安装对应版本Bazel

今天下载的tensorflow对应的commit为5a6c3d2d139547b19fe8ed5ae856ed8c6ebbd7f7，bazel对应的版本为4.2.2。tflite-soc的docker上没有正确版本的bazel，因此需要安装，步骤如下（此处默认在docker container里，拥有根用户权限，否则部分命令行需要添加sudo）：

首先安装依赖

apt-get install build-essential openjdk-11-jdk python zip unzip

若报错找不到openjdk-11-jdk，则进行如下设置

add-apt-repository ppa:openjdk-r/ppa
apt-get update
apt-get install openjdk-11-jdk

若继续报错ModuleNotFoundError: No module named 'apt_pkg'，则进行以下操作

cd /usr/lib/python3/dist-packages
ln -s apt_pkg.cpython-35m-x86_64-linux-gnu.so apt_pkg.so

然后从华为云下载对应的安装文件（此处是bazel-4.2.2-installer-linux-x86_64.sh）

安装Bazel

参考知乎文章Bazel 4.0.0在Linux下的安装，进行如下操作：
注意！以上命令会覆盖原有的bazel，小心使用！

chmod +x bazel-<version>-installer-linux-x86_64.sh
sudo ./bazel-<version>-installer-linux-x86_64.sh

修改对应的tensorflow-lite源代码

下图是TFLITE-SOC所用的tensorflow版本（添加了对于SystemC的支持以及若干示例）与官网版本的不同

我们以此为蓝图更改最新版本的tensorflow

添加示例

将./tensorflow/tensorflow/lite/examples/systemc和./tensorflow/tensorflow/lite/kernels/modeling拷贝到对应位置

添加SystemC库

打开./tensor/tensor/workspace2.bzl文件，添加如下定义：

tf_http_archive( name = "systemc", build_file = "//third_party:systemc.BUILD", sha256 = "5781b9a351e5afedabc37d145e5f7edec08f3fd5de00ffeb8fa1f3086b1f7b3f", urls = tf_mirror_urls("https://www.accellera.org/images/downloads/standards/systemc/systemc-2.3.3.tar.gz"), )

修改kernels/BUILD文件

打开./tensorflow/tensorflow/kernels/BUILD文件，用以下代码替换405行-442行

cc_library(name = "cpu_backend_gemm_lib",hdrs = ["cpu_backend_gemm.h","cpu_backend_gemm_params.h",],copts = tflite_copts(),deps = [#"//tensorflow/lite/kernels/internal:common","//tensorflow/lite/kernels/internal:compatibility",#"//tensorflow/lite/kernels/internal:types",],
)cc_library(name = "cpu_backend_gemm",srcs = ["cpu_backend_gemm_custom_gemv.h","cpu_backend_gemm_eigen.cc","cpu_backend_gemm_eigen.h","cpu_backend_gemm_gemmlowp.h","cpu_backend_gemm_ruy.h","cpu_backend_gemm_x86.h",],# hdrs = [#     "cpu_backend_gemm.h",#     "cpu_backend_gemm_params.h",# ],copts = tflite_copts(),deps = [":tflite_with_ruy","//tensorflow/lite/kernels/internal:common","//tensorflow/lite/kernels/internal:compatibility","//tensorflow/lite/kernels/internal:types",":cpu_backend_context",":cpu_backend_threadpool",# Isolated so it can be used by systemc"//tensorflow/lite/kernels:cpu_backend_gemm_lib",# Depend on ruy regardless of `tflite_with_ruy`. See the comment in# cpu_backend_gemm.h about why ruy is the generic path."@ruy//ruy","@ruy//ruy:matrix","@ruy//ruy:path","@ruy//ruy/profiler:instrumentation",# We only need to depend on gemmlowp and Eigen when tflite_with_ruy# is false, but putting these dependencies in a select() seems to# defeat copybara's rewriting rules."@gemmlowp","//third_party/eigen3","//tensorflow/lite/kernels/modeling:util"],
)

简单解释

SystemC示例修改了cpu_backend_gemm.h和cpu_backend_gemm_lowp.h的部分代码，用来打印matrix信息，所以此处将库函数cpu_backend_gemm_lib提出来分开编译了一下，对整个项目的结构并没有什么本质的改变。值得注意的是，src和hdrs分别对应编译cpu_backend_gemm库所需的源文件和头文件，需要包含cpu_backend_gemm.h所申明的所有文件（相比于TFLITE-SOC，最新版本添加了cpu_backend_gemm_x86.h文件，因此需要在srcs里添加此头文件）。

修改kernel代码

修改cpu_backend_gemm.h

1. 申明头文件

#ifdef TOGGLE_TFLITE_SOC
#include "tensorflow/lite/kernels/modeling/util.sc.h"
#endif

2. 添加matrix打印信息（#ifdef TOGGLE_TFLITE_SOC和#endif之间的代码）

/* Public entry point */
template <typename LhsScalar, typename RhsScalar, typename AccumScalar,typename DstScalar, QuantizationFlavor quantization_flavor>
void Gemm(const MatrixParams<LhsScalar>& lhs_params, const LhsScalar* lhs_data,const MatrixParams<RhsScalar>& rhs_params, const RhsScalar* rhs_data,const MatrixParams<DstScalar>& dst_params, DstScalar* dst_data,const GemmParams<AccumScalar, DstScalar, quantization_flavor>& params,CpuBackendContext* context) {
/** 此处省略中间代码*/
#ifdef TOGGLE_TFLITE_SOC// GemmImpl::Run(...) function executes a GEMM with different libraries// depending on the context.//// For quantized inputs it will use gemmlowp library// For floating pointing inputs it will use eigen librarytflite_soc::PrintMatricesInfo<LhsScalar, RhsScalar, DstScalar>(lhs_params, rhs_params, dst_params);
#endif// Generic case: dispatch to any backend as a general GEMM.GemmImpl<LhsScalar, RhsScalar, AccumScalar, DstScalar,quantization_flavor>::Run(lhs_params, lhs_data, rhs_params, rhs_data,dst_params, dst_data, params, context);
}

修改cpu_backend_gemm_gemmlowp.h

template <typename LhsScalar, typename RhsScalar, typename AccumScalar,typename DstScalar>
struct GemmImplUsingGemmlowp<LhsScalar, RhsScalar, AccumScalar, DstScalar,QuantizationFlavor::kIntegerWithUniformMultiplier> {static_assert(std::is_same<LhsScalar, RhsScalar>::value, "");static_assert(std::is_same<AccumScalar, std::int32_t>::value, "");using SrcScalar = LhsScalar;/** 此处省略中间代码*/using BitDepthParams = typename GemmlowpBitDepthParams<SrcScalar>::Type;#ifdef TOGGLE_TFLITE_SOC// printf("%d,%d,%d\n", lhs_params.rows, lhs_params.cols, rhs_params.cols);tflite_soc::PrintMatrices<LhsScalar, RhsScalar, DstScalar>(lhs_params, lhs_data, rhs_params, rhs_data, dst_params, dst_data);
#endifif (params.bias) {ColVectorMap bias_vector(params.bias, lhs_params.rows);gemmlowp::OutputStageBiasAddition<ColVectorMap> bias_addition_stage;
/** 此处省略中间代码*/#ifdef TOGGLE_TFLITE_SOCprintf("\nout after OP execution (%d,%d)\n", dst_params.rows,dst_params.cols);tflite_soc::PrintMatrix<DstScalar>(dst_params, dst_data);
#endif}
};

至此，所有需要修改的部分都已经完成了，其他版本的tensorflow也可对照以上方法实现SystemC支持（使用bazel编译的时候可能会有报错，但基本都是依赖问题，根据报错修改对应的BUILD文件即可）。

现在可以运行tflite-soc上的所有的SystemC的示例

# Hello World example
bazel build --jobs 1 //tensorflow/lite/examples/systemc:hello_systemc
bazel run //tensorflow/lite/examples/systemc:hello_systemc# 2D Systolic array accelerator in isolation
bazel build --jobs 1 //tensorflow/lite/kernels/modeling:systolic_run
bazel run //tensorflow/lite/kernels/modeling:systolic_run# 2D Systolic array accelerator with TFLite benchmarking tools
bazel build //tensorflow/lite/tools/benchmark:benchmark_model --cxxopt=-DTOGGLE_TFLITE_SOC=1
bazel-bin/tensorflow/lite/tools/benchmark/benchmark_model \--use_gpu=false --num_threads=1 --enable_op_profiling=true \--graph=../tensorflow-models/mobilenet-v1/mobilenet_v1_1.0_224_quant.tflite \--num_runs=1