TensorFlow Lite Micro: Embedded Machine Learning for TinyML Systems

Part of Proceedings of Machine Learning and Systems 3 pre-proceedings (MLSys 2021)

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Authors

Robert David, Jared Duke, Advait Jain, Vijay Janapa Reddi, Nat Jeffries, Jian Li, Nick Kreeger, Ian Nappier, Meghna Natraj, Tiezhen Wang, Pete Warden, Rocky Rhodes

Abstract

We introduce TensorFlow (TF) Micro, an open-source machine learning inference framework for running deep-learning models on embedded systems. TF Micro tackles the efficiency requirements imposed by embedded system resource constraints and the fragmentation challenges that make cross-platform interoperability nearly impossible. The framework adopts a unique interpreter-based approach that provides flexibility while overcoming the challenges. This paper explains the design decisions behind TF Micro and describes its implementation. We present an evaluation to demonstrate its low resource requirement and minimal run-time performance overhead.