Intelligence at the Extreme Edge: A Survey on Reformable TinyML

by   Visal Rajapakse, et al.

The rapid miniaturization of Machine Learning (ML) for low powered processing has opened gateways to provide cognition at the extreme edge (E.g., sensors and actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research field proposes to democratize the use of Machine Learning (ML) and Deep Learning (DL) on frugal Microcontroller Units (MCUs). MCUs are highly energy-efficient pervasive devices capable of operating with less than a few Milliwatts of power. Nevertheless, many solutions assume that TinyML can only run inference. Despite this, growing interest in TinyML has led to work that makes them reformable, i.e., work that permits TinyML to improve once deployed. In line with this, roadblocks in MCU based solutions in general, such as reduced physical access and long deployment periods of MCUs, deem reformable TinyML to play a significant part in more effective solutions. In this work, we present a survey on reformable TinyML solutions with the proposal of a novel taxonomy for ease of separation. Here, we also discuss the suitability of each hierarchical layer in the taxonomy for allowing reformability. In addition to these, we explore the workflow of TinyML and analyze the identified deployment schemes and the scarcely available benchmarking tools. Furthermore, we discuss how reformable TinyML can impact a few selected industrial areas and discuss the challenges and future directions.


Rethinking Machine Learning Development and Deployment for Edge Devices

Machine learning (ML), especially deep learning is made possible by the ...

A Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques

The union of Edge Computing (EC) and Artificial Intelligence (AI) has br...

Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey

The last decade, (2012 - 2022), saw an unprecedented advance in machine ...

A Machine Learning-oriented Survey on Tiny Machine Learning

The emergence of Tiny Machine Learning (TinyML) has positively revolutio...

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a m...

WiFi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Wireless local area networks (WLANs) empowered by IEEE 802.11 (WiFi) hol...

Implementations in Machine Ethics: A Survey

Increasingly complex and autonomous systems require machine ethics to ma...

Please sign up or login with your details

Forgot password? Click here to reset