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Paper Title

TinyML:machine learning on micro controllers

Article Identifiers

Registration ID: IJNRD_322103

Published ID: IJNRD2603358

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Keywords

TinyML and Microcontrollers

Abstract

TinyML (Tiny Machine Learning) is an emerging field that focuses on designing, training, optimizing, and deploying machine learning models that can run on extremely small and lowpower devices such as microcontrollers (MCUs) and embedded systems. Unlike traditional machine learning systems that require powerful computers, high memory capacity, and cloudbased processing, TinyML enables intelligent data processing directly on small devices with limited computing resources. These devices typically have very low memory, minimal processing power, and operate on battery power, making efficient and lightweight machine learning models essential. The main objective of TinyML is to bring artificial intelligence to edge devices so that data can be processed locally instead of being sent continuously to cloud servers. By performing computation directly on the device, TinyML reduces latency, improves privacy, lowers power consumption, and decreases dependency on internet connectivity. This allows devices to operate even in remote or offline environments. TinyML models are usually optimized to use only a few kilobytes of memory and consume very little energy, making them suitable for continuous or “always-on” applications. TinyML is widely used in various applications such as wake-word detection in voice assistants, gesture recognition in wearable devices, anomaly detection in industrial sensors, environmental monitoring, and visual object detection. For example, a smart device can recognize a specific voice command without sending audio data to the cloud, or a sensor can detect unusual patterns in machinery and trigger alerts instantly. These capabilities make systems faster, more secure, and more efficient. Another important advantage of TinyML is its role in advancing the Internet of Things (IoT). IoT devices generate large amounts of data, and sending all this data to the cloud for processing can consume significant bandwidth and energy. TinyML enables these devices to analyze data locally, sending only relevant results or alerts to the cloud when necessary. This reduces network load and improves system performance.

How To Cite (APA)

JAMES TOMY, JESNA K V, & REENA CHERIAN (March-2026). TinyML:machine learning on micro controllers. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(3), d438-d442. https://ijnrd.org/papers/IJNRD2603358.pdf

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Other Publication Details

Paper Reg. ID: IJNRD_322103

Published Paper Id: IJNRD2603358

Research Area: Other area not in list

Author Type: Indian Author

Country: idukki, kerala, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2603358.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2603358

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Publication Timeline

Paper Submission
16-03-2026
Peer Review
Through Scholar9.com Platform
Paper Acceptance
21-03-2026
Paper Publication
24-03-2026

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