Implementing artificial intelligence in low-power embedded devices presents several challenges. Limited processing power, memory constraints, and energy efficiency are key hurdles that restrict complex computations. These devices often lack the resources to run advanced algorithms in real-time, requiring highly optimized models. Additionally, ensuring data privacy and security with minimal hardware support can be difficult. Connectivity issues may further limit the ability to offload tasks to the cloud. Developers must balance performance with power consumption, which requires specialized knowledge and tools. To overcome these limitations, individuals can benefit from enrolling in a comprehensive embedded system course.
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