论文标题
微控制器的实时神经网络实施建议
Real-time Neural Networks Implementation Proposal for Microcontrollers
论文作者
论文摘要
使用人工神经网络（ANN）嵌入了用于实时应用程序中的人工神经网络（ANN）的智能系统目前面临着诸如物联网（IoT）（IoT）和Machine到Machine（M2M）等领域的需求不断增长。但是，由于处理其基本操作所需的高计算能力，ANN在这种系统中的应用构成了重大挑战。本文旨在在微控制器（低成本，低功率平台）中展示多层感知器（MLP）类型神经网络的实施策略。实现了具有完整分类过程的基于模块化矩阵的MLP，也实现了微控制器中的反向传播培训。测试和验证是通过训练过程的平方误差（MSE）的循环（HIL）中的硬件，分类结果以及每个实现模块的处理时间进行的。结果表明，超参数的值与分类所需的处理时间之间存在线性关系，并且处理时间与上述字段上许多应用程序所需的时间同意。这些发现表明，该实施策略和该平台可以成功应用于需要ANN功能的实时应用程序。
The adoption of intelligent systems with Artificial Neural Networks (ANNs) embedded in hardware for real-time applications currently faces a growing demand in fields like the Internet of Things (IoT) and Machine to Machine (M2M). However, the application of ANNs in this type of system poses a significant challenge due to the high computational power required to process its basic operations. This paper aims to show an implementation strategy of a Multilayer Perceptron (MLP) type neural network, in a microcontroller (a low-cost, low-power platform). A modular matrix-based MLP with the full classification process was implemented, and also the backpropagation training in the microcontroller. The testing and validation were performed through Hardware in the Loop (HIL) of the Mean Squared Error (MSE) of the training process, classification result, and the processing time of each implementation module. The results revealed a linear relationship between the values of the hyperparameters and the processing time required for classification, also the processing time concurs with the required time for many applications on the fields mentioned above. These findings show that this implementation strategy and this platform can be applied successfully on real-time applications that require the capabilities of ANNs.
