SDK release 12 Aug 2024

Author: janjongboom

.DS_Store

@@ -49,17 +49,6 @@
 #endif
 #endif // EI_CLASSIFIER_TFLITE_ENABLE_CMSIS_NN
 
-// CMSIS-NN falls back to reference kernels when __ARM_FEATURE_DSP and __ARM_FEATURE_MVE are not defined
-// we should never use those... So disable CMSIS-NN in that case and throw a warning
-#if EI_CLASSIFIER_TFLITE_ENABLE_CMSIS_NN == 1
-    #if !defined(__ARM_FEATURE_DSP) && !defined(__ARM_FEATURE_MVE)
-        #pragma message( \
-            "CMSIS-NN enabled, but neither __ARM_FEATURE_DSP nor __ARM_FEATURE_MVE defined. Falling back.")
-        #undef EI_CLASSIFIER_TFLITE_ENABLE_CMSIS_NN
-        #define EI_CLASSIFIER_TFLITE_ENABLE_CMSIS_NN 0
-    #endif
-#endif // EI_CLASSIFIER_TFLITE_ENABLE_CMSIS_NN == 1
-
 #if EI_CLASSIFIER_TFLITE_ENABLE_CMSIS_NN == 1
 #define CMSIS_NN                                     1
 #define EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES   1

CMSIS/.DS_Store

@@ -28,32 +28,32 @@
 
 /**
  * @defgroup ei_structs Structs
- * 
+ *
  * Public-facing structs for Edge Impulse C++ SDK.
- * 
+ *
  * @addtogroup ei_structs
  * @{
  */
 
 /**
  * @brief Holds the output of inference, anomaly results, and timing information.
- * 
+ *
  * `ei_impulse_result_t` holds the output of `run_classifier()`. If object detection is
  * enabled, then the output results is a
  * pointer to an array of bounding boxes of size `bounding_boxes_count`, as given by
  * [ei_impulse_result_bounding_box_t](https://docs.edgeimpulse.com/reference/ei_impulse_result_bounding_box_t).
  * Otherwise, results are stored as an array of classification scores, as given by
  * [ei_impulse_result_classification_t](https://docs.edgeimpulse.com/reference/ei_impulse_result_classification_t).
- * 
+ *
  * If anomaly detection is enabled (e.g. `EI_CLASSIFIER_HAS_ANOMALY == 1`), then the
  * anomaly score will be stored as a floating point value in `anomaly`.
- * 
- * Timing information is stored in an 
+ *
+ * Timing information is stored in an
  * [ei_impulse_result_timing_t](https://docs.edgeimpulse.com/reference/ei_impulse_result_timing_t)
  * struct.
- * 
+ *
  * **Source**: [classifier/ei_classifier_types.h](https://github.com/edgeimpulse/inferencing-sdk-cpp/blob/master/classifier/ei_classifier_types.h)
- * 
+ *
  * **Example**: [standalone inferencing main.cpp](https://github.com/edgeimpulse/example-standalone-inferencing/blob/master/source/main.cpp)
  */
 typedef struct {
@@ -70,16 +70,16 @@ typedef struct {
 
 /**
  * @brief Holds the output of visual anomaly detection (FOMO-AD)
- * 
+ *
  * If visual anomaly detection is enabled (e.g. `EI_CLASSIFIER_HAS_VISUAL_ANOMALY ==
- * 1`), then the output results will be a pointer to an array of grid cells of size 
- * `visual_ad_count`, as given by 
+ * 1`), then the output results will be a pointer to an array of grid cells of size
+ * `visual_ad_count`, as given by
  * [ei_impulse_result_bounding_box_t](https://docs.edgeimpulse.com/reference/ei_impulse_result_bounding_box_t).
- * 
+ *
  * The visual anomaly detection result is stored in `visual_ad_result`, which contains the mean and max values of the grid cells.
- * 
+ *
  * **Source**: [classifier/ei_classifier_types.h](https://github.com/edgeimpulse/inferencing-sdk-cpp/blob/master/classifier/ei_classifier_types.h)
- * 
+ *
  * **Example**: [standalone inferencing main.cpp](https://github.com/edgeimpulse/example-standalone-inferencing/blob/master/source/main.cpp)
 */
 typedef struct {
@@ -96,7 +96,7 @@ typedef struct {
 
 /**
  * @brief Holds information for a single bounding box.
- * 
+ *
  * If object detection is enabled (i.e. `EI_CLASSIFIER_OBJECT_DETECTION == 1`), then
  * inference results will be one or more bounding boxes. The bounding boxes with the
  * highest confidence scores (assuming those scores are equal to or greater than
@@ -105,20 +105,20 @@ typedef struct {
  * least `EI_CLASSIFIER_OBJECT_DETECTION_COUNT`. The exact number of bounding boxes
  * is stored in `bounding_boxes_count` field of [ei_impulse_result_t]/C++ Inference
  * SDK Library/structs/ei_impulse_result_t.md).
- * 
- * A bounding box is a rectangle that ideally surrounds the identified object. The 
+ *
+ * A bounding box is a rectangle that ideally surrounds the identified object. The
  * (`x`, `y`) coordinates in the struct identify the top-left corner of the box.
  * `label` is the predicted class with the highest confidence score. `value` is the
  * confidence score between [0.0..1.0] of the given `label`.
- * 
+ *
  * **Source**: [classifier/ei_classifier_types.h](https://github.com/edgeimpulse/inferencing-sdk-cpp/blob/master/classifier/ei_classifier_types.h)
- * 
+ *
  * **Example**: [standalone inferencing main.cpp](https://github.com/edgeimpulse/example-standalone-inferencing/blob/master/source/main.cpp)
 */
 typedef struct {
     /**
-     * Pointer to a character array describing the associated class of the given 
-     * bounding box. Taken from one of the elements of 
+     * Pointer to a character array describing the associated class of the given
+     * bounding box. Taken from one of the elements of
      * `ei_classifier_inferencing_categories[]`.
      */
     const char *label;
@@ -151,19 +151,19 @@ typedef struct {
 
 /**
  * @brief Holds timing information about the processing (DSP) and inference blocks.
- * 
+ *
  * Records timing information during the execution of the preprocessing (DSP) and
  * inference blocks. Can be used to determine if inference will meet timing requirements
  * on your particular platform.
- * 
+ *
  * **Source**: [classifier/ei_classifier_types.h](https://github.com/edgeimpulse/inferencing-sdk-cpp/blob/master/classifier/ei_classifier_types.h)
- * 
+ *
  * **Example**: [standalone inferencing main.cpp](https://github.com/edgeimpulse/example-standalone-inferencing/blob/master/source/main.cpp)
  */
 typedef struct {
     /**
      * If using `run_impulse()` to perform sampling and inference, it is the amount of
-     * time (in milliseconds) it took to fetch raw samples. Not used for 
+     * time (in milliseconds) it took to fetch raw samples. Not used for
      * `run_classifier()`.
      */
     int sampling;
@@ -185,12 +185,12 @@ typedef struct {
     int anomaly;
 
     /**
-     * Amount of time (in milliseconds) it took to run the post-processing block
+     * Amount of time (in microseconds) it took to run the post-processing block
      */
     int64_t dsp_us;
 
     /**
-     * Amount of time (in milliseconds) it took to run the inference block
+     * Amount of time (in microseconds) it took to run the inference block
      */
     int64_t classification_us;
 
@@ -203,23 +203,23 @@ typedef struct {
 
 /**
  * @brief Holds the output of inference, anomaly results, and timing information.
- * 
+ *
  * `ei_impulse_result_t` holds the output of `run_classifier()`. If object detection is
  * enabled (e.g. `EI_CLASSIFIER_OBJECT_DETECTION == 1`), then the output results is a
  * pointer to an array of bounding boxes of size `bounding_boxes_count`, as given by
- * [ei_impulse_result_bounding_box_t](https://docs.edgeimpulse.com/reference/ei_impulse_result_bounding_box_t). 
+ * [ei_impulse_result_bounding_box_t](https://docs.edgeimpulse.com/reference/ei_impulse_result_bounding_box_t).
  * Otherwise, results are stored as an array of classification scores, as given by
  * [ei_impulse_result_classification_t](https://docs.edgeimpulse.com/reference/ei_impulse_result_classification_t).
- * 
+ *
  * If anomaly detection is enabled (e.g. `EI_CLASSIFIER_HAS_ANOMALY == 1`), then the
  * anomaly score will be stored as a floating point value in `anomaly`.
- * 
- * Timing information is stored in an 
- * [ei_impulse_result_timing_t](https://docs.edgeimpulse.com/reference/ei_impulse_result_timing_t) 
+ *
+ * Timing information is stored in an
+ * [ei_impulse_result_timing_t](https://docs.edgeimpulse.com/reference/ei_impulse_result_timing_t)
  * struct.
- * 
+ *
  * **Source**: [classifier/ei_classifier_types.h](https://github.com/edgeimpulse/inferencing-sdk-cpp/blob/master/classifier/ei_classifier_types.h)
- * 
+ *
  * **Example**: [standalone inferencing main.cpp](https://github.com/edgeimpulse/example-standalone-inferencing/blob/master/source/main.cpp)
  */
 typedef struct {
@@ -238,13 +238,18 @@ typedef struct {
      * Array of classification results. If object detection is enabled, this will be
      * empty.
      */
+#ifdef EI_DSP_RESULT_OVERRIDE
+    // For CI only.  We will create the array to hold results
+    ei_impulse_result_classification_t* classification;
+#else
 #if EI_CLASSIFIER_LABEL_COUNT == 0
     // EI_CLASSIFIER_LABEL_COUNT can be 0 for anomaly only models
     // to prevent compiler warnings/errors, we need to have at least one element
     ei_impulse_result_classification_t classification[1];
 #else
     ei_impulse_result_classification_t classification[EI_CLASSIFIER_LABEL_COUNT];
-#endif
+#endif // EI_CLASSIFIER_LABEL_COUNT == 0
+#endif // EI_DSP_RESULT_OVERRIDE else
 
     /**
      * Anomaly score. If anomaly detection is not enabled, this will be 0. A higher

classifier/ei_classifier_config.h

@@ -374,15 +374,24 @@ __attribute__((unused)) static EI_IMPULSE_ERROR fill_result_struct_f32(const ei_
                                                                        ei_impulse_result_t *result,
                                                                        float *data,
                                                                        bool debug) {
-    for (uint32_t ix = 0; ix < impulse->label_count; ix++) {
+#ifdef EI_DSP_RESULT_OVERRIDE
+    uint32_t stop_count = EI_DSP_RESULT_OVERRIDE;
+#else
+    uint32_t stop_count = impulse->label_count;
+#endif
+    for (uint32_t ix = 0; ix < stop_count; ix++) {
+
         float value = data[ix];
 
         if (debug) {
             ei_printf("%s:\t", impulse->categories[ix]);
             ei_printf_float(value);
             ei_printf("\n");
         }
+// For testing purposes, we will have more values than labels
+#ifndef EI_DSP_RESULT_OVERRIDE
         result->classification[ix].label = impulse->categories[ix];
+#endif
         result->classification[ix].value = value;
     }
 

classifier/ei_classifier_types.h

@@ -42,6 +42,7 @@
 #define EI_CLASSIFIER_SYNTIANT                   10
 #define EI_CLASSIFIER_ONNX_TIDL                  11
 #define EI_CLASSIFIER_MEMRYX                     12
+#define EI_CLASSIFIER_ETHOS_LINUX                13
 
 #define EI_CLASSIFIER_SENSOR_UNKNOWN             -1
 #define EI_CLASSIFIER_SENSOR_MICROPHONE          1
@@ -108,7 +109,7 @@ typedef struct {
     extract_fn_t extract_fn;
     void *config;
     uint8_t *axes;
-    size_t axes_size;
+    uint8_t axes_size;
     int version;  // future proof, can easily add to this struct now
     DspHandle* (*factory)(void* config, float sampling_freq); // nullptr means no state
     // v1 ends here
@@ -126,7 +127,7 @@ typedef struct {
     void *config;
     int image_scaling;
     const uint32_t* input_block_ids;
-    const uint32_t input_block_ids_size;
+    const uint8_t input_block_ids_size;
     uint32_t output_features_count;
 } ei_learning_block_t;
 
@@ -149,6 +150,21 @@ typedef struct {
     size_t arena_size;
 } ei_config_tflite_graph_t;
 
+typedef struct {
+    uint16_t implementation_version;
+    uint8_t input_datatype;
+    bool input_quantized;
+    float input_scale;
+    float input_zeropoint;
+    uint8_t output_datatype;
+    bool output_quantized;
+    float output_scale;
+    float output_zeropoint;
+    const unsigned char *model;
+    size_t model_size;
+    size_t arena_size;
+} ei_config_ethos_graph_t;
+
 typedef struct {
     uint16_t implementation_version;
     TfLiteStatus (*model_init)(void*(*alloc_fnc)(size_t, size_t));
@@ -208,7 +224,9 @@ typedef struct ei_impulse {
     uint32_t project_id;
     const char *project_owner;
     const char *project_name;
-    uint32_t deploy_version;
+    uint16_t impulse_id;
+    const char *impulse_name;
+    uint16_t deploy_version;
 
     /* DSP details */
     uint32_t nn_input_frame_size;
@@ -220,7 +238,7 @@ typedef struct ei_impulse {
     uint32_t input_frames;
     float interval_ms;
     float frequency;
-    size_t dsp_blocks_size;
+    uint8_t dsp_blocks_size;
     ei_model_dsp_t *dsp_blocks;
 
     /* object detection */
@@ -229,20 +247,20 @@ typedef struct ei_impulse {
     uint32_t tflite_output_features_count;
 
     /* learning blocks */
-    const size_t learning_blocks_size;
+    const uint8_t learning_blocks_size;
     const ei_learning_block_t *learning_blocks;
 
     /* inference parameters */
-    uint32_t inferencing_engine;
+    uint8_t inferencing_engine;
 
     /* sensors and on-device inference */
-    uint32_t sensor;
+    uint8_t sensor;
     const char *fusion_string;
     uint32_t slice_size;
-    uint32_t slices_per_model_window;
+    uint8_t slices_per_model_window;
 
     /* output details */
-    uint16_t has_anomaly;
+    uint8_t has_anomaly;
     uint16_t label_count;
     const ei_model_performance_calibration_t calibration;
     const char **categories;