Changes between Version 86 and Version 87 of Other/Summer/2023/Inference

Timestamp:

Jul 20, 2023, 3:20:40 PM (12 months ago)

Author:

tm897

Comment:

—

Other/Summer/2023/Inference

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 <p><span style="color:#800000; font-weight: bold;">Team:</span> Shreya Venugopaal, Haider Abdelrahman, Tanushree Mehta, Lakshya Gour, Yunhyuk Chang</p>
 
-<p><span style="color:#800000; font-weight: bold;">Overview:</span> As machine learning models become increasingly advanced and complex, running these models on less powerful devices is becoming increasingly difficult, especially when accounting for latency. However, it is also not efficient to run everything using only the cloud as it creates too much traffic. A viable solution to this problem is edge computing, where we use the edge (the networks in between user devices and the cloud) for computation. 
-
 
 <p><span style="color:#800000; font-weight: bold;">Objective:</span> The purpose of this project is to use ORBIT in order to design experiments which will analyze the training and prediction of various ML models across multiple edge devices. Additionally, students will develop a latency profiling framework for MEC-assisted machine learning using cutting edge techniques such as splitting model computing and early exit. They will then analyze these models for latency and accuracy tradeoff analysis, along with measuring network delays.</p> 
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 }}}
+== Overview
+As machine learning models become increasingly advanced and complex, running these models on less powerful devices is becoming increasingly difficult, especially when accounting for latency. However, it is also not efficient to run everything using only the cloud as it creates too much traffic. A viable solution to this problem is edge computing, where we use the edge (the networks in between user devices and the cloud) for computation. 
 
 == Week 1