SDK for devices

SDK implements FogML [https://github.com/tszydlo/FogML] machine learning algorithms for resource-constrained devices.

Available modules and algorithms:

• anomaly_rt

  • reservoir sampling, Local Outliner Factor

• classifier

  • header file for the source code generated with FogML

• dsp

  • digital signal processing algorithms for initial time series analysis

• ports

  • code needed to port the SDK to various frameworks - supports Arduino and Zephyr OS

• rl

  • header files for the reinforcement learning source code generated with FogML

DSP Processing logic

DSP Processing blocks

#define TINYML_DSP_FFT  1 //FFT 1 peak
#define TINYML_DSP_BASE  2 // (max-min), mean
#define TINYML_DSP_VARIANCE  3 //variance
#define TINYML_DSP_ENERGY  4 //energy
#define TINYML_DSP_CROSSINGS  5 //crossing through mean
#define TINYML_DSP_MINMAX  6 //min, max

Each block generates the predefined number of features for each axis:

#define TINYML_DSP_FFT_LEN  2 // amp, freq
#define TINYML_DSP_BASE_LEN  2 // (max-min), mean
#define TINYML_DSP_VARIANCE_LEN  1 //variance
#define TINYML_DSP_ENERGY_LEN  1 //energy
#define TINYML_DSP_CROSSINGS_LEN  1 //crossing through mean
#define TINYML_DSP_MINMAX_LEN  2 //min, max

DSP Block configuration

//BLOCK 1 - BASE
tinyml_block_base_config_t block1_config;
tinyml_dsp_block_t block1 = {
    .type = TINYML_DSP_BASE,
    .config = &block1_config
};

//BLOCK 2 - ENERGY
tinyml_block_energy_config_t block2_config;
tinyml_dsp_block_t block2 = {
    .type = TINYML_DSP_ENERGY,
    .config = &block2_config
};

//BLOCK 3 - CROSSINGS
tinyml_block_crossings_config_t block3_config = {
    .threshold = 0.01
};
tinyml_dsp_block_t block3 = {
    .type = TINYML_DSP_CROSSINGS,
    .config = &block3_config
};

DSP configuration

// DATA ACQUISITION
// accelerometer data X, Y, Z
#define ACC_TIME_TICKS  64
#define ACC_AXIS  3

// number of features - depends on the DSP blocks
#define FOGML_VECTOR_SIZE ((TINYML_DSP_BASE_LEN + TINYML_DSP_ENERGY_LEN + TINYML_DSP_CROSSINGS_LEN) * ACC_AXIS)

//DSP config
tinyml_dsp_block_t *blocks_tab[] = {&block1, &block2, &block3};

tinyml_dsp_config_t my_dsp_config = {
    .time_ticks = ACC_TIME_TICKS,
    .axis_n = 3,
    .blocks_num = 3,
    .blocks = blocks_tab
};

SDK usage

void fogml_classification(float *time_series_data) {
    float *vector = (float*)malloc(sizeof(float) * FOGML_VECTOR_SIZE);
    tinyml_dsp(time_series_data, vector, &my_dsp_config);
    int cl;

    cl = classifier(vector);

#ifdef FOGML_VERBOSE
    fogml_printf("Detected  class = ");
    fogml_printf_int(cl);
    fogml_printf("\n");
#endif

    free(vector);
}