“TensorFlow Lite for Microcontrollers (TFLM): Recent Developments,” a Presentation from BDTI and Google

David Davis, Senior Embedded Software Engineer, and John Withers, Automation and Systems Engineer, both of BDTI, present the “TensorFlow Lite for Microcontrollers (TFLM): Recent Developments” tutorial at the May 2022 Embedded Vision Summit.

TensorFlow Lite Micro (TFLM) is a generic inference framework designed to run TensorFlow models on digital signal processors (DSPs), microcontrollers and other embedded targets with small memory footprints and very low power usage. TFLM aims to be easily portable to various embedded targets from those running an RTOS to bare-metal code. TFLM leverages the model optimization tools from the TensorFlow ecosystem and has additional embedded-specific optimizations to reduce the memory footprint. TFLM also integrates with a number of community contributed optimized hardware-specific kernel implementations.

In this talk, Davis and Withers review collaboration between BDTI and Google over the last year, including porting nearly two dozen operators from TensorFlow Lite to TFLM, creation of a separate Arduino examples repository, improved testing and documentation of both Arduino and Colab training examples and transitioning TFLM’s open-source CI framework to use GitHub Actions.

See here for a PDF of the slides.

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