TinyML Software Stacks Overview: Tools for Running AI on Microcontrollers

TinyML Updated on August 14, 2025

Introduction

The rise of TinyML — machine learning that runs directly on microcontrollers and other resource-constrained devices — is transforming the way we think about AI deployment. Instead of sending data to the cloud for processing, TinyML enables on-device intelligence with ultra-low power consumption, minimal memory usage, and real-time decision-making.

But there’s a catch: these tiny chips are nothing like the GPUs and high-performance CPUs used in conventional AI systems. They have kilobytes of RAM, clock speeds measured in tens or hundreds of MHz, and strict energy budgets. Running AI in such environments is only possible thanks to carefully designed software stacks — combinations of tools, libraries, and frameworks optimized for tiny hardware.

In this post, we’ll explore the main software stacks powering TinyML today, understand how they work, and see what makes them unique for microcontroller-based AI applications.

The Role of Software Stacks in TinyML

A software stack in TinyML is a layered set of tools that work together to move a model from training on a desktop or cloud environment to running efficiently on a microcontroller.

A typical TinyML stack includes:

  • Model development and training tools – Usually based on mainstream ML frameworks like TensorFlow, PyTorch, or scikit-learn, often running on a PC or cloud server.
  • Model optimization tools – These reduce size, memory footprint, and compute requirements through quantization, pruning, and architecture changes.
  • Inference engines – Lightweight runtime libraries that execute the optimized model on embedded hardware.
  • Hardware abstraction and drivers – Interfaces that allow the inference engine to work with the microcontroller’s peripherals, sensors, and memory systems.

Unlike traditional AI software, where models run on powerful and general-purpose hardware, TinyML stacks are highly specialized to squeeze every cycle and byte of memory for efficiency.

Over the past few years, several software stacks have emerged as leaders in the TinyML ecosystem. They differ in programming language, hardware compatibility, and optimization strategies, but all aim for the same goal: running AI on devices with extremely limited resources.

TensorFlow Lite for Microcontrollers (TFLM)

Overview TensorFlow Lite for Microcontrollers is Google’s lightweight inference engine for running TensorFlow models on MCUs. It’s part of the TensorFlow Lite ecosystem but is specifically stripped down for devices with as little as 16 KB of RAM.

Key Features

  • Supports 8-bit quantized models for minimal memory usage.
  • Written in C++ for portability across many microcontrollers.
  • No dynamic memory allocation after initialization, avoiding heap fragmentation.
  • Compatible with ARM Cortex-M, ESP32, RISC-V MCUs, and more.

Workflow Developers usually train a model in TensorFlow on a PC, convert it to TensorFlow Lite format, apply quantization, and then deploy it with the TFLM runtime on the target device.

Use Cases TFLM has been used for keyword spotting, gesture recognition, anomaly detection in industrial sensors, and low-power image classification.

Edge Impulse

Overview Edge Impulse is more than just an inference engine — it’s a full end-to-end TinyML platform. It provides a web-based environment where developers can collect data, train models, optimize them, and deploy directly to a wide range of MCUs.

Key Features

  • Integrated data acquisition from connected devices.
  • Automatic model optimization with quantization-aware training.
  • Supports multiple inference engines, including TFLM and CMSIS-NN.
  • Device SDKs for C++, Arduino, and Zephyr RTOS.

Workflow Edge Impulse abstracts away much of the manual optimization process. Once a model is trained, developers can export firmware that includes the complete inference stack for the target device.

Use Cases Great for rapid prototyping of sensor-based applications such as environmental monitoring, wearable activity tracking, and predictive maintenance.

CMSIS-NN

Overview CMSIS-NN, developed by Arm, is not a full ML framework but a collection of highly optimized neural network kernels for Arm Cortex-M processors. It’s designed to speed up inference by leveraging the hardware features of ARM MCUs.

Key Features

  • Optimized for fixed-point arithmetic, ideal for quantized models.
  • Significantly reduces compute cycles compared to generic C implementations.
  • Can be integrated with TFLM or other inference engines.

Workflow Typically used as a backend for inference frameworks, where it replaces generic math operations with hardware-tuned kernels for faster performance.

Use Cases Applications where performance is critical and the target hardware is ARM-based, such as real-time vibration analysis or high-frequency audio classification.

Other Notable Stacks

  • Neuton (now part of Nordic Semiconductor) – Originally a standalone no-code TinyML platform, Neuton specialized in generating ultra-compact machine learning models — often under 5 KB — without requiring developers to write model code manually. In June 2025, Nordic Semiconductor acquired Neuton’s technology and IP, aiming to integrate its automated model generation capabilities into Nordic’s edge AI strategy. This acquisition allows developers using Nordic’s SoCs to quickly deploy highly optimized AI models with minimal memory footprint and power consumption. Integration with Nordic’s SDKs and developer tools is already underway.

  • uTensor – An early C++ framework for MCUs, now largely superseded by TensorFlow Lite for Microcontrollers but still used in some legacy projects.

  • Arduino Machine Learning – Arduino’s integration with TensorFlow Lite for Microcontrollers and Edge Impulse makes it possible to deploy TinyML models using the Arduino IDE, appealing to beginners and hobbyists.

  • EON Compiler (Edge Impulse) – An advanced compiler that takes existing ML models and further compresses them for ultra-low-power inference, making it possible to fit AI workloads into even smaller devices.

How These Stacks Work Together

In real-world TinyML projects, these stacks are often combined rather than used in isolation. For example:

  • TFLM + CMSIS-NN – TensorFlow Lite handles the model execution logic, while CMSIS-NN provides fast math operations for ARM chips.
  • Edge Impulse + TFLM – Edge Impulse provides the data collection and training platform, then exports firmware using TFLM as the runtime.

This modular approach allows developers to balance performance, portability, and development speed.

Choosing the Right TinyML Stack

Selecting the right stack depends on factors like:

  • Target hardware – ARM-based MCUs benefit from CMSIS-NN, while ESP32 developers may lean towards TFLM or Edge Impulse.
  • Model complexity – Larger models may require aggressive quantization or pruning.
  • Development workflow – If you want rapid prototyping, a platform like Edge Impulse might save significant time.
  • Optimization needs – For ultra-low power and tight memory budgets, quantization and fixed-point optimized stacks are essential.

In many cases, starting with TFLM is a safe bet, then layering in optimizations as you understand the hardware’s limitations.

The Future of TinyML Software Stacks

The TinyML ecosystem is evolving rapidly. Trends we can expect in the near future include:

  • Better integration with mainstream ML frameworks so developers can move from cloud to MCU with minimal friction.
  • Automated hardware-aware model optimization using AI-driven compilers that adapt models for each device automatically.
  • Expanded support for non-ARM architectures, including RISC-V and custom AI accelerators.
  • Standardized APIs that allow swappable inference engines without rewriting application code.

As hardware becomes more capable and tools more sophisticated, TinyML stacks will empower developers to deploy richer, smarter models directly on the smallest devices.

Conclusion

TinyML software stacks are the unsung heroes of embedded AI. Without them, running machine learning on microcontrollers would require enormous amounts of manual optimization and low-level coding.

From TensorFlow Lite for Microcontrollers and Edge Impulse to CMSIS-NN and Nordic Semiconductor’s newly acquired Neuton, each stack plays a crucial role in bridging the gap between powerful cloud-trained models and the resource constraints of tiny devices.

The best part? You don’t have to choose just one. Many TinyML projects benefit from combining these tools to achieve the perfect balance of speed, memory efficiency, and ease of development.

As the field matures, the future will bring even more streamlined, hardware-aware, and automated stacks — making it easier than ever to put AI in the palm of your hand.