Paper Title
TinyML:machine learning on micro controllers
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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.
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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
Issue
Volume 11 Issue 3, March-2026
Pages : d438-d442
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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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