Tiny Machine Learning: Progress and Futures

• URL: http://arxiv.org/abs/2403.19076v2
• Date: Fri, 29 Mar 2024 21:33:39 GMT
• Title: Tiny Machine Learning: Progress and Futures
• Authors: Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Song Han,
• Abstract summary: Tiny Machine Learning (TinyML) is a new frontier of machine learning. TinyML is challenging due to hardware constraints. We will first discuss the definition, challenges, and applications of TinyML.
• Score: 24.76599651516217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scope of AI applications and enable ubiquitous intelligence. However, TinyML is challenging due to hardware constraints: the tiny memory resource makes it difficult to hold deep learning models designed for cloud and mobile platforms. There is also limited compiler and inference engine support for bare-metal devices. Therefore, we need to co-design the algorithm and system stack to enable TinyML. In this review, we will first discuss the definition, challenges, and applications of TinyML. We then survey the recent progress in TinyML and deep learning on MCUs. Next, we will introduce MCUNet, showing how we can achieve ImageNet-scale AI applications on IoT devices with system-algorithm co-design. We will further extend the solution from inference to training and introduce tiny on-device training techniques. Finally, we present future directions in this area. Today's large model might be tomorrow's tiny model. The scope of TinyML should evolve and adapt over time.

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