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Inference speed comparison using convolutions in neural networks on various SoC hardware platforms using MicroPython

In recent years we have witnessed the rapid development of machine learning algorithms, and the same can be said for IoT. Developments in both fields have also influenced the growth of machine learning algorithms in IoT devices. The authors of a series of papers cite several reasons to argue this trend. This paper explores the possibility of using the Python programming language in different versions to create, train, and implement convolutional neural networks on two SoCs based on different architectures (ARM and RISC-V). The influence of the number of filters in the convolutional layer on the inference speed is also investigated. The number of filters has a different effect on inference speed depending on the existence of additional components that accelerate individual operations of convolutional neural networks (convolution, batch normalization, activation, and pooling operations).

CNN ; Convolution Neural Network ; MicroPython ; RISC-V ; SoC

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