Inexpensive Real-Time AI Solutions Using Low-Power Edge Devices

This paper explores the feasibility of deploying neural networks on low-cost embedded devices, using Raspberry Pi 5 as a platform for real-time digit recognition and generation.Leveraging accessible tools such as TensorFlow and Keras, a feed-forward neural network and a convolutional neural network (CNN) were implemented to classify handwritten digits from the MNIST dataset.A Raspberry PI Camera module enabled live digit recognition, demonstrating how AI navigated automation can be achieved with minimal hardware investment.Additionally, a generative adversarial network (GAN) was trained to produce synthetic handwritten digits, showcasing the creative potential of edge devices for content generation tasks.This proof-of-concept highlights how modern AI frameworks simplify the development and deployment of machine learning solutions on resource-constrained devices.The study underscores the potential of edge AI for business applications by offering energy-efficient, offline or online capable systems that enhance data privacy and reduce dependence on cloud infrastructure, enabling scalable and cost-effective automation in a variety of sectors.