To minimize delays and allow real-time automation, IoT data processing is moving from centralized cloud servers to smart edge devices capable of local decision-making. This is a systematic review of the feasibility and performance of the Raspberry Pi 4 Model B as an inexpensive system-on-chip regarding lightweight ML models and cluster system architectures in edge-based AI. Results showed that Pi 4B ML was extremely practical and energy-efficient in time- consuming applications, such as industrial predictive maintenance and remote medical supervision. More importantly, although Pi clusters can provide the cheap computation founda- tion, its scalable performance of Distributed Deep Learning is hindered by immaturity from existing frameworks. Benchmarks show that small neural networks can perform at very high speed by a single Pi 4B model compared to other general- purpose hardware of the same size. The paper does confirm that Pi 4B plays an essential role in facilitating cost-effective, decentralized intelligence; therefore, optimized algorithms and enhanced distributed architectures should be developed in order to unlock the complete scale potential of the Pi clusters for next- generation Distributed AI Systems.

In conclusion, the Raspberry Pi 4 Model B is a convenient and affordable device for running simple Machine Learning tasks directly on Internet of Things (IoT) devices. It is the perfect choice for edge computing, which means data processing occurs locally instead of in a far-off cloud. This shift is crucial because it reduces delays and enables real-time automation in various applications. Raspberry Pi 4B strengths for Edge AI: •Power and Efficiency: With its quad-core processor and up to 8 GB of RAM, the Pi 4B can easily handle simple machine learning tasks like predicting when a machine needs maintenance or remotely monitoring a patient’s health. It does this while being incredibly energy- efficient. •Speed and Value: For small neural networks, the Pi 4B offers high speed for its size and price. This makes it an ideal fit for quick-response systems in smart homes, healthcare care, and security devices.

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