A.I. NEURAL NETWORKS INFERENCE INTO THE IOT EMBEDDED DEVICES USING TINYML FOR PATTERN DETECTION WITHIN A SECURITY SYSTEM

Abstract

The paper presents the implementation challenges of a proof of concept development for image processing with artificial intelligence neural network into an embedded device. Because of the hardware constraints of the embedded device-e.g. 16 KB RAM and 128 KB of EEPROM, the deep learning/the training and the model of the neural network is processed in cloud-e.g. Google Cloud Platform-AI-Artificial intelligence, as shown in second section. After this step, the trained neural networks model, values and tensors are translated with TinyML into native code for the embedded devices and deployed on a specific hardware platform-e.g. Arduino Nano 33 BLE Sense or SparkFun Edge Development Board Apollo3 Blue-for the neural network inferences-e.g. person detection or NLP into specific area, as described in third section. The last section shows the conclusions and the security challenges for deploying neural networks into embedded systems which are used for security systems such as: monitoring and surveillance cameras, drones visual computing for securing field areas, IoT systems, etc.