AI Inference Optimization: Bridging the Gap Between Cloud and Edge Processing
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The rapid spread of AI across various fields like IoT, autonomous systems, and real-time analytics has brought to light the need for smarter ways to handle data processing both in the cloud and at the edge. While cloud computing is known for its powerful processing capabilities, it often depends on stable internet connections and it can deal with high latency, making it less suitable for applications that need in stant responses. On the other hand, edge devices excel in providing quick responses but typically face limits in processing power and energy. This research dives into techniques that can effectively connect the strengths of both cloud and edge computing. By looking at strategies like dynamic workload distribution, model compression, and flexible resource management, the aim is to find a balance between speed, cost, and energy use. Ultimately, the goal is to create a scalable and adaptable framework for AI that allows for the smooth operation of models across different systems.
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