TinyML: Analysis of Xtensa LX6 microprocessor for Neural Network Applications by ESP32 SoC
This work addresses performance evaluation for tinyML on embedded IoT devices, but it is incremental as it focuses on benchmarking an existing microprocessor.
The paper analyzes the speed of the Xtensa LX6 microprocessor in ESP32 SoCs for neural network applications, testing performance with varying inputs and hidden layers in feed-forward mode.
In recent decades, Machine Learning (ML) has become extremely important for many computing applications. The pervasiveness of ultra-low-power embedded devices such as ESP32 or ESP32 Cam with tiny Machine Learning (tinyML) applications will enable the mass proliferation of Artificial Intelligent powered Embedded IoT Devices. In the last few years, the microcontroller device (Espressif ESP32) became powerful enough to be used for small/tiny machine learning (tinyML) tasks. The ease of use of platforms like Arduino IDE, MicroPython and TensorFlow Lite (TF) with tinyML application make it an indispensable topic of research for mobile robotics, modern computer science and electrical engineering. The goal of this paper is to analyze the speed of the Xtensa dual core 32-bit LX6 microprocessor by running a neural network application. The different number of inputs (9, 36, 144 and 576) inputted through the different number of neurons in neural networks with one and two hidden layers. Xtensa LX6 microprocessor has been analyzed because it comes inside with Espressif ESP32 and ESP32 Cam which are very easy to use, plug and play IoT device. In this paper speed of the Xtensa LX6 microprocessor in feed-forward mode has been analyzed.