Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)

doi:10.1109/ACCESS.2022.3207200

Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)

2022-01-01

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Syed Ali Raza Zaidi *

DOI：10.1109/ACCESS.2022.3207200

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Machine Learning (ML) on the edge is key to enabling a new breed of IoT and autonomous system applications. The departure from the traditional cloud-centric architecture means that new deployments can be more power-efficient, provide better privacy and reduce latency for inference. At the core of this paradigm is TinyML, a framework allowing the execution of ML models on low-power embedded devices. TinyML allows importing pre-trained ML models on the edge for providing ML-as-a-Service (MLaaS) to IoT devices. This article presents a TinyMLaaS (TMLaaS) architecture for future IoT deployments. The TMLaaS architecture inherently presents several design trade-offs in terms of energy consumption, security, privacy, and latency. We also present how TMLaaS architecture can be implemented, deployed, and maintained for large-scale IoT deployment. The feasibility of implementation for the TMLaaS architecture has been demonstrated with the help of a case study.

Keywords:

Internet of Things

Logic gates

Cloud computing

Performance evaluation

Computational modeling

Memory management

Machine learning

Edge computing

Internet of Things

Deep learning

Transfer learning

Collaborative work

Energy efficiency

Tiny machine learning

IoT

edge computing

5G

LoRa

gesture recognition

deep learning

transfer learning

federated learning

implementation

MLOps

energy efficiency

Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)

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