MAB on hardware

Please use this identifier to cite or link to this item: http://repository.iiitd.edu.in/xmlui/handle/123456789/1507

Full metadata record

DC Field	Value	Language
dc.contributor.author	Pareek, Animesh
dc.contributor.author	Darak, Sumit Jagdish (Advisor)
dc.date.accessioned	2024-05-17T09:30:39Z
dc.date.available	2024-05-17T09:30:39Z
dc.date.issued	2023-11-29
dc.identifier.uri	http://repository.iiitd.edu.in/xmlui/handle/123456789/1507
dc.description.abstract	The Multi Armed Bandit (MAB) problem in the Reinforcement Learning field refers to the problem of allocating resources in certain choices for achieving an overall goal with the objective of maximizing expected gain. Our BTP Journey is based on the goal of using MAB algorithms on hardware to effectively achieve better designs of hardware accelerators for various problems. In this semester we have focussed on learning about UCB (Upper confidence Bound) algorithm and its role in Early-Exiting of Deep Neural networks (with split computing) [SplitEE].	en_US
dc.language.iso	en_US	en_US
dc.publisher	IIIT-Delhi	en_US
dc.subject	Split Computing	en_US
dc.subject	Early-Exiting	en_US
dc.subject	Deep Neural Network	en_US
dc.subject	Multi Armed Bandit	en_US
dc.subject	Upper confidence Bound	en_US
dc.subject	Reinforcement Learning	en_US
dc.subject	SplitEE	en_US
dc.title	MAB on hardware	en_US
dc.type	Other	en_US
Appears in Collections:	Year-2023

Files in This Item:

File	Description	Size	Format
BTP sem-1 Report - Animesh Pareek.pdf Restricted Access		897.94 kB	Adobe PDF	View/Open Request a copy

DSpace JSPUI