APP_Framework/Framework/:update knowing framework

1.fix some Kconfig file
2.add tensorflow-lite-for-mcu in knowing file
3.add mnist application,note the application cannot be used with RAM less than 500K.
4.the version need to separate application and OS(rtt),later by using add transform layer to solve it.

APP_Framework/Applications/knowing_app/mnist/main.cpp

@@ -0,0 +1,94 @@
+#include <rtthread.h>
+#include <stdio.h>
+
+#include "tensorflow/lite/micro/all_ops_resolver.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+#include "tensorflow/lite/micro/micro_interpreter.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+
+#include "digit.h"
+#include "model.h"
+
+namespace {
+tflite::ErrorReporter* error_reporter = nullptr;
+const tflite::Model* model = nullptr;
+tflite::MicroInterpreter* interpreter = nullptr;
+TfLiteTensor* input = nullptr;
+TfLiteTensor* output = nullptr;
+constexpr int kTensorArenaSize = 110 * 1024;
+uint8_t *tensor_arena = nullptr;
+//uint8_t tensor_arena[kTensorArenaSize];
+}
+
+extern "C" void mnist_app() {
+  tflite::MicroErrorReporter micro_error_reporter;
+  error_reporter = &micro_error_reporter;
+
+  model = tflite::GetModel(mnist_model);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    TF_LITE_REPORT_ERROR(error_reporter,
+                         "Model provided is schema version %d not equal "
+                         "to supported version %d.",
+                         model->version(), TFLITE_SCHEMA_VERSION);
+    return;
+  }
+
+  tensor_arena = (uint8_t *)rt_malloc(kTensorArenaSize);
+  if (tensor_arena == nullptr) {
+    TF_LITE_REPORT_ERROR(error_reporter, "malloc for tensor_arena failed");
+    return;
+  }
+
+  tflite::AllOpsResolver resolver;
+  tflite::MicroInterpreter static_interpreter(
+      model, resolver, tensor_arena, kTensorArenaSize, error_reporter);
+  interpreter = &static_interpreter;
+
+  // Allocate memory from the tensor_arena for the model's tensors.
+  TfLiteStatus allocate_status = interpreter->AllocateTensors();
+  if (allocate_status != kTfLiteOk) {
+    TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
+    return;
+  }
+
+  input = interpreter->input(0);
+  output = interpreter->output(0);
+
+  printf("------- Input Digit -------\n");
+  for (int i = 0; i < 28; i++) {
+    for (int j = 0; j < 28; j++) {
+      if (mnist_digit[i*28+j] > 0.3)
+        printf("#");
+      else
+        printf(".");
+    }
+    printf("\n");
+  }
+
+  for (int i = 0; i < 28*28; i++) {
+        input->data.f[i] = mnist_digit[i];
+    }
+
+  TfLiteStatus invoke_status = interpreter->Invoke();
+  if (invoke_status != kTfLiteOk) {
+    TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed on x_val\n");
+    return;
+  }
+
+  // Read the predicted y value from the model's output tensor
+  float max = 0.0;
+  int index;
+  for (int i = 0; i < 10; i++) {
+         if(output->data.f[i]>max){
+           max = output->data.f[i];
+           index = i;
+         }
+  }
+  printf("------- Output Result -------\n");
+  printf("result is %d\n", index);
+}
+
+extern "C" {
+MSH_CMD_EXPORT(mnist_app, run mnist app);
+}