Implementation of Gaussian Naive Bayes classification on STM32 ARM Cotex M3 microcontroller using CMSIS-DSP (CMSIS 5.7.0) library. The project is written in Keil uVision 5.
The training of the classifier was done using Scikit Learn package in Python 3. The script can be found here. The training data generated are three concatenated random cluster of points(100 vectors) having 2 dimension that can be represented on a cartesian plane. The classifier was trained on these points and four Gaussian parameters were generated. The parameters are Gaussian averages, Gaussian variances, Class priors and Epsilon range.
This parameters were used in the firmware for inferencing of random data points generated in the ARM microcontroller. After each classification at 10ms interval an led at PB12 is toggled as an indication. Classification data is shown as output in a serial terminal. The GPIO and UART peripheral can be disabled to save 150 µA.
The inference result is validated and the result can be seen below:
The Gaussian classifier was used to classify randomly user defined points and the result is represented as classes. The classes are named as 0,1 and 2 which can pictorially be assigned on the points cluster A, B and C respectively. The pictorial representation of cluster of points is shown below:
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System Clock: HSI 8 MHz and HSE 8MHz(Highest 72 MHz). HSE crystal frequency can be as low as 4Mhz which is lowest for STM32F103xB.
- Reason: Low Power consumption and future scope of USB implementation. USB, as per the spec, requires the frequency to be within 0.25% (2500 ppm) of the ideal value. In the datasheet of STM32F103xB (chapter 5.3.7) HSI is guaranteed to have a tolerance of -1.1% to +1.8% at 25°C. The test of the project was done with 8Mhz clock frequency due to limited hardware resource.
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Average Current Consumption: 4.1mA with UART Peripheral enabled(Current consumption: 97.76 µA), Core clock frequency at 8Mhz and a step duration for 10ms. The power consumption can be optimised by switching to STANDBY mode after each conversion and then waking the MCU which requires LSE clock for accurate wakeup.
A power consumption profile for RUN Mode by step is shown below:
