MCU Simulation Software Algorithm based on Deep Learning from Single Network Model to Multiple Layer Scenarios

Ruilin Li, Guang Mei
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Abstract

MCU simulation software algorithm based on deep learning from single network model to multiple layer scenarios is designed and implemented in the paper. For the realization of neural network in MCU, the selection and realization of the activation function is one of the key issues. Since the bipolar S-function has both a very good linear region and a very good nonlinear region, it can process both small and large signals at the same time. During the wiring process of the specific equipment, the audio and the video signals are 2-way signals, which must be connected to the input and output respectively, and the designed system is based on this operation. Therefore, in the software simulation system, it must also have these functions, and support the software compilation and also debugging environment. Furthermore, the designed model is also tested under the extreme scenarios.
基于深度学习的单片机仿真软件算法,从单网络模型到多层场景
本文设计并实现了基于深度学习的从单网络模型到多层场景的单片机仿真软件算法。在单片机中实现神经网络,激活函数的选择与实现是关键问题之一。由于双极s函数既有很好的线性区域,又有很好的非线性区域,所以它可以同时处理小信号和大信号。在具体设备的布线过程中,音频和视频信号是双向信号,必须分别连接到输入和输出,设计的系统就是基于这个操作。因此,在软件仿真系统中,也必须具备这些功能,并支持软件的编译和调试环境。此外,还对所设计的模型在极端情况下进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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