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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2508.15008 |
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| _version_ | 1866912805456707584 |
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| author | Abushahla, Hamza A. Varam, Dara Panopio, Ariel Justine N. AlHajri, Mohamed I. |
| author_facet | Abushahla, Hamza A. Varam, Dara Panopio, Ariel Justine N. AlHajri, Mohamed I. |
| contents | The deployment of Quantized Neural Networks (QNNs) on resource-constrained edge devices, such as microcontrollers (MCUs), introduces fundamental challenges in balancing model performance, computational complexity, and memory constraints. Tiny Machine Learning (TinyML) addresses these issues by jointly advancing machine learning algorithms, hardware architectures, and software optimization techniques to enable deep neural network inference on embedded systems. This survey provides a hardware-oriented perspective on neural network quantization, systematically reviewing the quantization methods most relevant to MCUs and extreme-edge devices. Particular emphasis is placed on the critical trade-offs between model performance and the capabilities of MCU-class hardware, including memory hierarchies, numerical representations, and accelerator support. The survey further reviews contemporary MCU hardware platforms, including ARM-based and RISC-V-based designs, as well as MCUs integrating neural processing units (NPUs) for low-precision inference, together with the supporting software stacks. In addition, we analyze real-world deployments of quantized models on MCUs and consolidate the application domains in which such systems are used. Finally, we discuss open challenges and outline promising future directions toward scalable, energy-efficient, and sustainable AI deployment on edge devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications Abushahla, Hamza A. Varam, Dara Panopio, Ariel Justine N. AlHajri, Mohamed I. Machine Learning Artificial Intelligence Hardware Architecture The deployment of Quantized Neural Networks (QNNs) on resource-constrained edge devices, such as microcontrollers (MCUs), introduces fundamental challenges in balancing model performance, computational complexity, and memory constraints. Tiny Machine Learning (TinyML) addresses these issues by jointly advancing machine learning algorithms, hardware architectures, and software optimization techniques to enable deep neural network inference on embedded systems. This survey provides a hardware-oriented perspective on neural network quantization, systematically reviewing the quantization methods most relevant to MCUs and extreme-edge devices. Particular emphasis is placed on the critical trade-offs between model performance and the capabilities of MCU-class hardware, including memory hierarchies, numerical representations, and accelerator support. The survey further reviews contemporary MCU hardware platforms, including ARM-based and RISC-V-based designs, as well as MCUs integrating neural processing units (NPUs) for low-precision inference, together with the supporting software stacks. In addition, we analyze real-world deployments of quantized models on MCUs and consolidate the application domains in which such systems are used. Finally, we discuss open challenges and outline promising future directions toward scalable, energy-efficient, and sustainable AI deployment on edge devices. |
| title | Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications |
| topic | Machine Learning Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2508.15008 |