EMEA 2021 Student Forum: Utilizing Static Code Generation in TinyML

EMEA 2021 Student Forum
Utilizing Static Code Generation in TinyML
Rafael STAHL, PhD Candidate, Technical University of Munich

The deployment of machine learning applications on micro-controllers known as TinyML enables advanced, low-power applications. Major challenges are posed by these resource-constrained devices in terms of run time, memory usage and safety. Existing machine learning frameworks provide runtime libraries that are weak in those aspects because they dynamically load and execute models. In this talk, we present deployment flows based on TensorFlow Lite for Microcontrollers and TVM, that improve these aspects through static code generation. The presented static code generator flows already provide some features that will ease the use in safety-critical software systems. Yet, there are still open challenges in deploying generated code from existing deployment flows in according to automotive safety standards, that will be discussed shortly in the talk. The code generators were evaluated on the TinyMLPerf benchmark and on average reduced the run time by 3.0x in TVM, working memory by 1.37x and read-only memory by 1.54x in TFLite Micro.


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