AIoT Solution Survey and Comparison in Machine Learning on Lowcost Microcontroller Neural Networks, especially Convolutional Neural Network 1 are becoming increasingly popular in IoT edge devices today for executing data analytics right at the source without transmitting to Cloud Computing centers. So that it will be reduced latency as well as energy consumption for data communication. In this paper, we will compare CMSISNN and uTensor: low energy consumption microcontrollers. Most classification tasks have alwayson, and realtime requirements, which limits the total number of operations per neural network inference. So that, with image classification model, microcontrollers will execute lower frame per second than GPU and embedded CPU. CMSISNN is a collection of efficient kernels which was developed to maximize the performance and minimize the memory footprint of Neural Network applications. Allow deploy machine learning models on ARM CortexM processors for intelligent IoT edge devices.

Artificial Vision on Edge IoT Devices: A Practical Case for 3D Data Classification Nowadays, with the huge advance of sensor technology and the increase of the amount of data generated by them, techniques have to be developed to be able to process all this amount of information in realtime applications on edge devices, close to where data is being generated. If all that information has to be sent to the cloud to be processed, it has certain disadvantages in terms of latency, bandwidth, privacy and reliability, compared to locally processing it on the edge. In this paper, the implementation of deep learning algorithms in low power and limited resources devices in an Internet of Things scenario is studied. In order to work in realtime applications, the influence of different low power consumption deep learning hardware accelerators is studied. Finally, a practical case for smart farming is shown with comparative results in terms of power consumption and performance when running the same artificial vision algorithm on different devices.