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Many embedded AI systems require real-time processing capabilities, as they need to respond quickly to changing conditions. This is crucial for applications like self-driving cars or medical devices.
Energy efficiency is a significant consideration for embedded AI systems, as many operate on battery power or in environments where power is limited. Optimizing algorithms and hardware for energy efficiency is essential.
Some embedded AI systems use specialized hardware accelerators like GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) to improve the performance of AI-related tasks.
Embedded AI solutions often need to be highly customized to the specific requirements of the application and the limitations of the embedded hardware.
Security is a critical concern when integrating AI into embedded systems, especially in applications like IoT devices. Proper encryption and data protection are essential.