DigitalCommons@Kennesaw State University - C-Day Computing Showcase: GRP-134 Characterizing and Understanding the Performance of Small Language Models on Edge Devices

 

Presenter Information

MD ROMYULL ISLAMFollow

Location

https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php

Streaming Media

Event Website

https://github.com/Romyull-Islam/SLM

Document Type

Event

Start Date

15-4-2025 4:00 PM

Description

In recent years, significant advancements in computing power, data richness, algorithmic development, and the growing demand for applications have catalyzed the rapid emergence and proliferation of large language models (LLMs) across various scenarios. Concurrently, factors such as computing resource limitations, cost considerations, real-time application requirements, task-specific customization, and privacy concerns have also driven the development and deployment of small language models (SLMs). Unlike extensively researched and widely deployed LLMs in the cloud, the performance of SLM workloads and their resource impact on edge environments remain poorly understood. More detailed studies will have to be carried out to understand the advantages, constraints, performances, and resource consumption in different settings of the edge.

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Apr 15th, 4:00 PM

GRP-134 Characterizing and Understanding the Performance of Small Language Models on Edge Devices

https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php

In recent years, significant advancements in computing power, data richness, algorithmic development, and the growing demand for applications have catalyzed the rapid emergence and proliferation of large language models (LLMs) across various scenarios. Concurrently, factors such as computing resource limitations, cost considerations, real-time application requirements, task-specific customization, and privacy concerns have also driven the development and deployment of small language models (SLMs). Unlike extensively researched and widely deployed LLMs in the cloud, the performance of SLM workloads and their resource impact on edge environments remain poorly understood. More detailed studies will have to be carried out to understand the advantages, constraints, performances, and resource consumption in different settings of the edge.

https://digitalcommons.kennesaw.edu/cday/Spring_2025/PhD_Research/10