Integrating AI with Edge Computing and Cloud Services for Real-Time Data Processing and Decision Making
DOI:
https://doi.org/10.47709/ijmdsa.v2i1.2559Keywords:
Edge Computing, Multimodal AI, Real-Time Decision-making, AI Optimization, Model Pruning, Quantization, AI AcceleratorsAbstract
Connecting Multimodal AI and Edge computing for better real-time decision making: a paper on their synergy Edge computing is a solution that allows to overcome latency issue and processing data closer to the source, Multimodal AI on the other hand integrates and analyzes different types of data (images, audio, sensor data, etc.) to provide richer insights. Such a combination has a strong significance in autonomous vehicles and healthcare monitoring applications which require timely decision making with informed decisions. However, there are some inherent limitations to edge devices in computational power, energy expense, and data confidentiality. The paper examines several optimization methods such as model pruning that reduces model size, quantization that decreases the limit of precision, and domain specific AI accelerators to increase the processing speed to counteract these difficulties. The purpose of these strategies is to get a complex AI model to deploy on an edge device with limited computing resources at the cost of minimum performance. Combining Multimodal AI with edge computing can potentially transform data driven real-time decision-making applications across various fields. As Development of hardware and software never stops, formulated boundaries continue to expand, enabling more intelligent and responsive systems.
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