The term “edge AI” refers to performing artificial intelligence inference on hardware with limited resources, such as microcontrollers, leading to a growing interest in this domain. Despite the focus on running AI on such constrained platforms, there is a scarcity of insights into their actual capabilities, which this series aims to address. The discussion centers on TensorFlow Lite for Microcontrollers (tflite-micro, now LiteRT for Microcontrollers), emphasizing how models’ operational components, like operators and kernels, influence inference performance. With optimizations such as employing specific processor instructions (via ARM architecture extensions), tflite-micro can enhance operations like addition. Future explorations will delve into model encoding in .tflite files and the intricacies of kernel invocation, while showcasing the transition from basic implementations to those utilizing advanced hardware acceleration, demonstrating varying execution efficiencies. This comprehensive analysis aims to demystify the functional layers of AI inference on constrained hardware systems.
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