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Tensorflow Lite in Zephyr on LiteX/VexRiscv

By December 9, 2019May 12th, 2021No Comments

While much of the focus for the recent developments in AI has been on cloud-centric implementations, there are many use cases where AI algorithms have to be run on small and resource constrained devices. Google’s TensorFlow Lite, a smaller brother of one of the world’s most popular Machine Learning frameworks, is focused on exactly that – running neural network inference on resource constrained devices. A more recent but very exciting effort, led by Pete Warden’s team at Google in collaboration with partners like ARM, Ambiq Micro, Sparkfun and Antmicro, aims to bring TF Lite to microcontrollers, with sub-milliwatt power envelopes and sub-dollar unit costs for an AI-enabled node.

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