TinyML：ESP32 SoC对Xtensa LX6微处理器神经网络应用的分析
TinyML: Analysis of Xtensa LX6 microprocessor for Neural Network Applications by ESP32 SoC
摘要：
近几十年来，机器学习（ML）对于许多计算应用已经变得非常重要。超低功耗嵌入式设备的普及，如ESP 32或ESP 32 Cam与微型机器学习（tinyML）应用程序，将使人工智能驱动的嵌入式物联网设备的大规模扩散。在过去的几年里，微控制器器件（Espressif ESP 32）变得足够强大，可以用于小型/微型机器学习（tinyML）任务。Arduino IDE、MicroPython和TensorFlow Lite（TF）等平台的易用性以及tinyML应用程序使其成为移动机器人、现代计算机科学和电气工程不可或缺的研究课题。本文的目标是通过运行一个神经网络应用程序来分析Xtensa双核32位LX 6微处理器的速度。通过具有一个和两个隐藏层的神经网络中的不同数量的神经元输入的不同数量的输入（9、36、144和576）。Xtensa LX 6微处理器已被分析，因为它内置Espressif ESP 32和ESP 32 Cam，非常易于使用，即插即用物联网设备。本文对Xtensa LX 6微处理器在前馈模式下的速度进行了分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
