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Developing a TinyML Image Classifier in an Hour
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AI
DOI：10.1109/OJIES.2024.3451959.png)
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Abstract
En 中文
Tiny machine learning technologies are bringing intelligence ever closer to the sensor, thus enabling the key benefits of edge computing (e.g., reduced latency, improved data security, higher energy efficiency, and lower bandwidth consumption, also without the need for constant connectivity). This promises to significantly enhance industrial applications but requires suited development tools to deal with the complexity of the edge technologies and context. We propose an agile Jupyter Python notebook as a simple, manageable tool to efficiently and effectively develop microcontroller-based intelligent imaging classification sensors. The notebook implements a methodology involving hyperparameter tuning and comparison of different shallow and deep learning models, with quantization. It exports TensorFlow Lite models, deployable on several microcontroller families, and optionally exploits the STM32Cube.AI developer cloud service, which allows benchmarking the developed models on a set of real-world tiny hardware target platforms. Assessment concerns various types of metrics, both for machine learning (e.g., accuracy) and embedded systems (e.g., memory footprint, latency, and energy consumption). We have verified the support for development effectiveness and efficiency on four ultralow resolution image-classification datasets, with different levels of input and task complexity. In all cases, the tool was able to build microcontroller-deployment ready, beyond the state-of-the-art models, within 1 h on Google Colab CPUs.
Keywords:
Quantization (signal)
Accuracy
Robot sensing systems
Computational modeling
Tiny machine learning
Training
Memory management
Edge computing
imaging sensors
Jupyter notebook
microcontroller units (MCUs)
neural networks (NNs)
quantization
tiny machine learning (TinyML)
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I
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4.3
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1.7K
Citations:
991