tensorflow/tflite-micro

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3059 stars · 1064 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TensorFlow Lite for Microcontrollers (TFLM) is a C++ port of TensorFlow Lite for running ML models on microcontrollers, DSPs, and other memory-constrained embedded devices. It provides a minimal inference runtime with ports for targets like Cortex-M, RISC-V, Hexagon, and Xtensa.

Use cases

• run a neural network on an Arduino or Cortex-M microcontroller
• deploy a keyword-spotting model to a DSP
• do on-device inference with only kilobytes of RAM
• port TensorFlow Lite models to embedded targets like RISC-V or Xtensa
• build tinyml applications on resource-constrained hardware

When to choose

• you need ML inference on microcontrollers or DSPs with very limited memory
• you want a maintained, hardware-ported runtime for embedded TensorFlow Lite models
• you are building tinyml/edge-ai applications without an OS or with minimal resources

When to avoid

• you need training or full TensorFlow tooling on servers or desktops
• your target has ample compute and memory, where standard TensorFlow Lite or a full framework is better
• you need GPU-accelerated or cloud inference

Facets

library · maturity active

machine-learning deep-learning llm-inference embedded machine-learning embedded-systems deep-learning iot cpp embedded iot cross-platform tensorflow-lite microcontrollers dsp edge-ai tinyml inference

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markdown · JSON · MCP: product_card(name="tensorflow/tflite-micro")