5.5.22-Efficient-AI-Song-Han

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  Modern deep learning requires a massive amount of computational resources, carbon footprint, and engineering efforts. On mobile devices, the hardware resource and power budget are very limited, and on-device machine learning is challenging; retraining the model on-device is even more difficult. We make machine learning efficient and fit tiny devices (TinyML). Our research is highlighted by full-stack optimizations, including the neural network topology, inference library, and the hardware architecture, which allows a larger design space to unearth the underlying principles.

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