"Embedded AI" refers to the integration of artificial intelligence (AI) capabilities into embedded systems. Embedded systems are specialized computer systems that are designed to perform dedicated functions or tasks within a larger system. These systems are typically resource-constrained, with limited processing power, memory, and sometimes, power sources.
The incorporation of AI into embedded systems enables these systems to perform more complex and intelligent tasks, making them capable of processing and analyzing data, making decisions, and adapting to changing conditions. Here are some key points about embedded AI:
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.