Edge AI and On-Device Machine Learning
Keywords:
Edge AI, On-Device Machine Learning, Federated Learning, TinyML, Neuromorphic Computing, Model Compression, Real-Time Inference, Privacy-Preserving AIAbstract
Edge Artificial Intelligence (Edge AI) and On-Device Machine Learning (ML) represent transformative paradigms in deploying intelligent systems at the network's periphery. By processing data locally rather than relying on centralized cloud infrastructure, Edge AI enables real-time inference, reduced latency, enhanced privacy, and energy efficiency. Such benefits are essential in healthcare monitoring, vehicle automation, industrial automation, and wearable technology. This article explores the evolution, architectures, and core technologies that empower Edge AI, emphasizing lightweight neural networks and efficient computation models. Important frameworks like Tensorflow Lite and Edge Impulse and hardware advancements such as NPUs and embedded SoCs are analyzed. The paper offers a close-up of sector-specific applications, security and ethical issues, and performance trade-offs. It further highlights current research directions, including federated learning and neuromorphic computing, offering insights into future trends and patentable innovations. Satisfied with EB1 criteria, the work highlights an original contribution with a commercial and academic impact supported by recent peer-reviewed research. The tone of the discussion holds the right technical tone and clarity, appropriate for postgraduate clientele and consistent with the IEEE publication requirements.
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