Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review
Abstract: The field of Tiny Machine Learning (TinyML) has gained significant attention due to its potential to enable intelligent applications on resource-constrained devices. This review provides an in-depth analysis of the advancements in efficient neural networks and the deployment of deep learning models on ultra-low-power microcontrollers (MCUs) for TinyML applications.
It begins by introducing neural networks and discussing their architectures and resource requirements. It then explores MEMS-based applications on ultra-low-power MCUs, highlighting their potential for enabling TinyML on resource-constrained devices.
The core of the review centres on efficient neural networks for TinyML. It covers techniques such as model compression, quantization, and low-rank factorization, which optimize neural network architectures for minimal resource utilization on MCUs.
The paper then delves into the deployment of deep learning models on ultra-low-power MCUs, addressing challenges such as limited computational capabilities and memory resources. Techniques like model pruning, hardware acceleration, and algorithm-architecture co-design are discussed as strategies to enable efficient deployment.
Lastly, the review provides an overview of current limitations in the field, including the trade-off between model complexity and resource constraints.
Overall, this review paper presents a comprehensive analysis of efficient neural networks and deployment strategies for TinyML on ultra-low-power MCUs. It identifies future research directions for unlocking the full potential of TinyML applications on resource-constrained devices.
Submission Length: Long submission (more than 12 pages of main content)
Assigned Action Editor: ~Brian_Kingsbury1
Submission Number: 2228
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