This article covers the benefits of deploying language models on edge / IoT Devices. We will also cover use cases of Language models on edge devices.
Language models , a sub field of artificial intelligence , have shown remarkable progress in natural language processing and understanding. Language models are algorithms designed to understand , generate and manipulate human language. They are trained on large amount of text data. Popular language models include GPT, BERT and T5. Leveraging language models in IoT applications opens numerous innovative possibilities across various sectors.
Running language models on for IoT applications can be approached in two primary ways: Locally on edge devices or remotely in the cloud or data centre. The choice between remote or locall execution depends on specific requirements and constraints of the use case.
Cloud execution may offer large computational resources to handle complex tasks but it presents challenges on data privacy, latency and robust network connectivity to transmit data between edge device and the cloud. On the other hand deploying language models directly on edge devices presents reduced latency, Enhanced privacy and security and bandwidth Efficiency.
But there are challenges running language models on Edge. Theser are restriction on Model size and storage and Power consumption; The different techniques used for running language models to be able to run on edge devices include Model compression, Quantisation, Edge specific Model architecture and using Hardware acceleration.
Use Cases of Language Models on Edge Devices:
Voice Assistants – Language models on edge devices can enhance the functionality of smart home systems by providing more sophisticated and intuitive control through voice commands.
Industrial Automation -Workers can use vocie commands to control machinery. Edge systems like washing machines can initiate predictive maintenance based on natural language inputs.
Healthcare Application – Edge device based language models can assist in patient monitoring, provide instant medical advice based on symptoms described and facilitate seamless communication with healthcare providers.
Gaming and Virtual reality – Players can communicate with game characters and navigate the virtual environment using natural language.
Smart Retail – Smart Kiosks and interactive displays can understand and respond to customer queries in natural language, recommend product based on preferences and even facilitate seamless checkouts.
Autonomous Vehicles – Passengers can interact with vehicles AI systems to control navigation, entertainment and climate settings creating more intuitive and user friendly experience.
Security and Surveillance – Analyse audio inputs in realtime to identify suspicious activities or unauthorised access attempts
Education and e-Learning – Can support in personalised learning experiences by providing real time feedback and assistance.
Smart TVs – By integrating NLP, smart TVs can understand and respond to voice commands, making it easer for users to search for content, adjust settings and receive personalised recommendations.
Accessibility tools – They can provide real time transcription services , translate sign language into text to speech and offer personalised assistance to mane technology inclusive and accessible.
Smart Phones – From Managing daily tasks such as setting reminders, sending messages, making calls, provide real time translations and personalised recommendations offering unparalleled level of convenience and efficiency.
As technology continues to evolve we can expect to see more innovative use cases and improved capabilities of Language models on edge devices.
Reference: OpenSource magazine (Jan 2025) article By Mr. Narasimha Sekhar Kakaraparthi.
