Because edge AI devices can do complex calculations locally, they usually need more power than embedded AI devices. This is because edge AI devices frequently require sophisticated processors or accelerators like GPUs or TPUs. Although real-time data processing is made possible, energy usage rises as a result. Embedded AI devices, on the other hand, are perfect for battery-operated or resource-constrained contexts since they prioritize low power consumption and are made for extremely narrow purposes.

Computational complexity, hardware design, and application requirements are some of the variables that affect power consumption. Both depend on effective power management, which guarantees performance without sacrificing energy efficiency.

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