Oral: TensorFlow Lite Micro: Embedded Machine Learning for TinyML Systems
Apr 4, 2021
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Robert David
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Jared Duke
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Advait Jain
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Vijay Janapa Reddi
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Nat Jeffries
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Nick Kreeger
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Ian Nappier
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Meghna Natraj
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Shlomi Regev
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Rocky Rhodes
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Rocky Rhodes
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Tiezhen Wang
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Pete Warden
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About
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.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 Micr…
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MLSys 2021
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About MLSys 2021
The Conference on Machine Learning and Systems targets research at the intersection of machine learning and systems. The conference aims to elicit new connections amongst these fields, including identifying best practices and design principles for learning systems, as well as developing novel learning methods and theory tailored to practical machine learning workflows.
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