How to integrate Neuton into your firmware project

Include header file

Copy all files from this archive to your project and include header file:

#include "neuton.h"

The library contains functions to get model information such as:

• task type (regression, classification, etc.);
• neurons and weights count;
• window buffer size;
• input and output features count;
• model size and RAM usage;
• float support flag;
• quantization level.

Main functions are:

• neuton_model_set_inputs - to set input values;
• neuton_model_run_inference - to make predictions.

Set input values

Make a float array with model inputs. Inputs count and order should be the same as in the training dataset.

float inputs[] = {
    feature_0,
    feature_1,
    // ...
    feature_N
};

Pass this array to neuton_model_set_inputs function.

If digital signal processing option was selected on platform you should call neuton_model_set_inputs multiple times for each sample to fill internal window buffer. Function will return 0 when buffer is full, this indicates that model is ready for prediction.

Make prediction

When buffer is ready you should call neuton_model_run_inference with two arguments:

• pointer to index of predicted class;
• pointer to neural net outputs (dimension of array can be read using neuton_model_outputs_count function).

For regression task output value will be stored at outputs[0]. For classifications task index will contain class index with maximal probability, outputs will contains probabilities of each class. Thus, you can get predicted class probability at outputs[index].

Function will return 0 on successful prediction.

if (neuton_model_set_inputs(inputs) == 0)
{
    uint16_t index;
    float* outputs;
    
    if (neuton_model_run_inference(&index, &outputs) == 0)
    {
        // code for handling prediction result
    }
}