---
title: 【開發資源】TinyML MCU 等級開源推論引擎
description: 開源推論引擎在人工智慧應用中扮演著關鍵角色，提供了多樣化且高效能的解決方案，以應對從嵌入式系統到大型資料中心的廣泛需求，本文將介紹TinyML MCU 等級開源推論引擎的連結資源。
url: "https://makerpro.cc/docs/tinyml-mcu-inference-engines/"
updated: "2025-06-09"
category: AI工具
---

# 【開發資源】TinyML MCU 等級開源推論引擎

作者：[Jack Hsu](https://makerpro.cc/jack-hsu-column/)

> 本文轉載自[歐尼克斯實境互動工作室(OmniXRI)](https://omnixri.blogspot.com/2025/06/tinyml-mcu.html)，2025.6.5 整理製作

開源推論引擎在人工智慧應用中扮演著關鍵角色，提供了多樣化且高效能的解決方案，以應對從嵌入式系統到大型資料中心的廣泛需求。這些引擎不僅支援各種硬體平台，還積極參與開源社群，促進了模型部署的標準化和跨平台互通。

舉例來說，Arm的CMSIS-6包括了NN和DSP模組，專為微控制器和DSP優化，而Google的LiteRT和TensorFlow Lite系列則強調在邊緣裝置上的低功耗高效運算。同時，PyTorch的Executorch和QNNPack以及Alibaba的MNN和TinyNeuralNetwork，以及Tencent的TNN和NCNN，都提供了多樣化的運算解決方案，涵蓋了從高效能計算到低功耗嵌入式系統的各種需求。

這開源專案透過其獨特的技術和演算法貢獻，豐富了開發者在機器學習和深度學習模型轉換及優化的工具選擇。這些引擎和工具的共同目標是推動AI技術在端點裝置上的普及，並促進其在各種應用領域中的實用化和擴展。以下整理一些連結資源供開發者朋友參考：

### 開源推論引擎

- Arm CMSIS-6 (NN, DSP)
   [https://github.com/ARM-software/CMSIS_6](https://github.com/ARM-software/CMSIS_6)
- Google LiteRT （TensorFlow Lite）
   [https://github.com/google-ai-edge/litert](https://github.com/google-ai-edge/litert)
- Google TensorFlow Lite for Microcontroller (TFLite-Micro)
   [https://github.com/tensorflow/tflite-micro](https://github.com/tensorflow/tflite-micro)
- PyTorch ExecuTorch
   [https://github.com/pytorch/executorch](https://github.com/pytorch/executorch)
- PyTorch QNNPack
   [https://github.com/pytorch/qnnpack](https://github.com/pytorch/qnnpack)
- Alibaba MNN
   [https://github.com/alibaba/MNN](https://github.com/alibaba/MNN)
- Alibaba TinyNeuralNetwork
   [https://github.com/alibaba/TinyNeuralNetwork](https://github.com/alibaba/TinyNeuralNetwork)
- Tencent TNN
   [https://github.com/Tencent/TNN](https://github.com/Tencent/TNN)
- Tencent NCNN
   [https://github.com/Tencent/ncnn](https://github.com/Tencent/ncnn)
- uTensor
   [https://github.com/uTensor/uTensor](https://github.com/uTensor/uTensor)
- Fraunhofer-IMS AIfES
   [https://github.com/Fraunhofer-IMS/AIfES_for_Arduino](https://github.com/Fraunhofer-IMS/AIfES_for_Arduino)
- Sipeed TinyMaix
   [https://github.com/sipeed/TinyMaix](https://github.com/sipeed/TinyMaix)
- XiaoMi MACE
   [https://github.com/XiaoMi/mace](https://github.com/XiaoMi/mace)
- PaddlePaddle Paddle-Lite
   [https://github.com/PaddlePaddle/Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite)
- Eloquent EloquentMicroML
   [https://github.com/eloquentarduino/EloquentMicroML](https://github.com/eloquentarduino/EloquentMicroML)
- Kraiskil ONNX2C
   [https://github.com/kraiskil/onnx2c](https://github.com/kraiskil/onnx2c)
- Huawei MindSpore
   [https://github.com/mindspore-ai/mindspore](https://github.com/mindspore-ai/mindspore)
- SenseTime OpenPPL (PPLNN)
   [https://github.com/OpenPPL/ppl.nn](https://github.com/OpenPPL/ppl.nn)
- Open AI Lab Tengine
   [https://github.com/OAID/Tengine](https://github.com/OAID/Tengine)

### AI 相關編譯器

- MicroTVM
   [https://tvm.hyper.ai/docs/topic/microtvm/](https://tvm.hyper.ai/docs/topic/microtvm/)
- Skymizer ONNC
   [https://github.com/ONNC/onnc](https://github.com/ONNC/onnc)
- Arm Ethos-U Vela Compiler
   [https://pypi.org/project/ethos-u-vela/](https://pypi.org/project/ethos-u-vela/)

### MCU 專用工具

- Edge Impulse
   [https://docs.edgeimpulse.com/docs](https://docs.edgeimpulse.com/docs)
- STM32
  - CubeAI (X-CUBE-AI)
     [https://stm32ai.st.com/stm32-cube-ai/](https://stm32ai.st.com/stm32-cube-ai/)
  - NanoEdge AI Studio
     [https://stm32ai.st.com/nanoedge-ai/](https://stm32ai.st.com/nanoedge-ai/)
  - ST Edge AI Suit
     [https://www.st.com/content/st_com/en/st-edge-ai-suite.html](https://www.st.com/content/st_com/en/st-edge-ai-suite.html)
- NXP eIQ
   [https://www.nxp.com/design/design-center/software/eiq-ai-development-environment:EIQ](https://www.nxp.com/design/design-center/software/eiq-ai-development-environment:EIQ)
- Renesas eAI
   [https://www.renesas.com/en/key-technologies/artificial-intelligence/e-ai/e-ai-development-environment-microcontrollers](https://www.renesas.com/en/key-technologies/artificial-intelligence/e-ai/e-ai-development-environment-microcontrollers)