IEEE Circuits and Systems Magazine - Q3 2023 - 8

Feature
IMAGE LICENSED BY INGRAM PUBLISHING
Tiny Machine Learning:
Progress and Futures
Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, and Song Han
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 the hardware constraints: the tiny memory
resource is difficult 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 codesign
the algorithm and system stack to enable TinyML. In this
Digital Object Identifier 10.1109/MCAS.2023.3302182
Date of current version: 11 October 2023
8
IEEE CIRCUITS AND SYSTEMS MAGAZINE
1531-636X/23©2023IEEE
THIRD QUARTER 2023
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 ondevice
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.
Index Terms-TinyML, efficient deep learning, on-device
training, learning on the edge.

IEEE Circuits and Systems Magazine - Q3 2023

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