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MCU Compatibility Checker

Select a use case, pick a framework, set constraints — see which microcontrollers fit.

Use Case
Framework
Connectivity

Select a use case to begin

Choose an AI use case from the dropdown to see compatible microcontrollers ranked by suitability.

Use Case
Framework
Connectivity

Select a use case to begin

Choose an AI use case from the dropdown to see compatible microcontrollers ranked by suitability.

How the Compatibility Checker Works

  1. Select your use case — Choose from object detection, voice recognition, predictive maintenance, and more. Each use case has specific hardware requirements for RAM, flash, and sensor inputs.
  2. Pick an AI framework — Choose between TensorFlow Lite Micro, Edge Impulse, or evaluate all. Framework support varies by MCU architecture.
  3. Get ranked results with reasoning — The checker scores each MCU based on RAM headroom, flash capacity, clock speed, hardware acceleration, and your constraints.

Learn more about choosing microcontrollers for machine learning or the differences between ESP32 and STM32 for AI workloads.

Frequently Asked Questions

Which microcontroller is best for running AI models?
It depends on the use case. The ESP32-S3 is the best all-rounder for edge AI thanks to 512 KB SRAM, vector instructions, and camera support. For demanding workloads like real-time object detection, the STM32H7 with 1 MB SRAM and Cortex-M7 at 480 MHz offers more headroom. For simple keyword spotting or gesture recognition, an Arduino Nano 33 BLE or ESP32-C3 can be sufficient.
Can you run TensorFlow on an ESP32?
Yes. TensorFlow Lite Micro supports the Xtensa LX6 (ESP32) and LX7 (ESP32-S3) architectures. You deploy quantized .tflite models that run inference on-device without cloud connectivity. The ESP32-S3 is preferred because its vector instructions accelerate neural network operations.
How much RAM does edge AI need?
Minimum requirements vary by use case. Keyword spotting needs ~128 KB SRAM. Gesture recognition and anomaly detection need ~64-128 KB. Object detection typically needs 256+ KB, ideally 512 KB or more. These figures cover the model, inference buffers, and application code.
What is the cheapest MCU for machine learning?
The ESP32-C3 is the most affordable option at $1-3 per chip ($4-10 for a dev board). It runs TensorFlow Lite Micro on its RISC-V core and handles simple models like anomaly detection and keyword spotting. For more demanding tasks, the ESP32 at $2-5 per chip is the next step up.

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EFR32MGESP32ESP32-C3ESP32-C6ESP32-S2ESP32-S3GAP8i.MX RT1052i.MX RT1062i.MX RT1064LPC55xxnRF52832nRF52833nRF52840RA6M5SAMD51SAME51STM32F3STM32F4STM32F7STM32G4STM32H5STM32H7STM32L4STM32L5STM32U5STM32WB

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