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Design of Reconfigurable Hardware CNN for Audio and Image Classification.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Design of Reconfigurable Hardware CNN for Audio and Image Classification./
Author:	Agbalessi, Christie.
Description:	1 online resource (796 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-03.
Contained By:	Masters Abstracts International84-03.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29326817click for full text (PQDT)
ISBN:	9798841781684

Design of Reconfigurable Hardware CNN for Audio and Image Classification.
Agbalessi, Christie.

Design of Reconfigurable Hardware CNN for Audio and Image Classification. - 1 online resource (796 pages)

Source: Masters Abstracts International, Volume: 84-03.

Thesis (M.S.)--Rochester Institute of Technology, 2022.

Includes bibliographical references

Picture yourself resting or attending a meeting in the back seat while your car is driving you home with all the privacy a driverless vehicle offers. Designing autonomous systems involves decoding environmental cues and making safe decisions. Convolution Neural Networks (CNNs) are the leading choices for computer vision tasks due to their high performance and scalability to pieces of hardware. They have long been run on Graphical Processing Units (GPUs) and Central Processing Units (CPUs). Yet, today, there is an urgent need to accelerate CNNs in low-power consumption hardware for real-time inference. This research aims to design a configurable hardware accelerator for 8-bit fixed point audio and image CNN models. An audio network is developed to classify environmental sounds from children playing in the streets, car horns, and sirens; an image network is designed to classify cars, lanes, road signs, traffic lights, and pedestrians. The two CNNs are quantized from a 32-bit floating-point to an 8-bit fixed-point format while maintaining high accuracy. The hardware accelerator is verified in SystemVerilog and compared to similar works.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841781684Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Autonomous vehiclesIndex Terms--Genre/Form:

542853
Electronic books.

Design of Reconfigurable Hardware CNN for Audio and Image Classification.
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500 $a Advisor: Indovina, Mark A.
502 $a Thesis (M.S.)--Rochester Institute of Technology, 2022.
504 $a Includes bibliographical references
520 $a Picture yourself resting or attending a meeting in the back seat while your car is driving you home with all the privacy a driverless vehicle offers. Designing autonomous systems involves decoding environmental cues and making safe decisions. Convolution Neural Networks (CNNs) are the leading choices for computer vision tasks due to their high performance and scalability to pieces of hardware. They have long been run on Graphical Processing Units (GPUs) and Central Processing Units (CPUs). Yet, today, there is an urgent need to accelerate CNNs in low-power consumption hardware for real-time inference. This research aims to design a configurable hardware accelerator for 8-bit fixed point audio and image CNN models. An audio network is developed to classify environmental sounds from children playing in the streets, car horns, and sirens; an image network is designed to classify cars, lanes, road signs, traffic lights, and pedestrians. The two CNNs are quantized from a 32-bit floating-point to an 8-bit fixed-point format while maintaining high accuracy. The hardware accelerator is verified in SystemVerilog and compared to similar works.
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538 $a Mode of access: World Wide Web
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650 4 $a Computer engineering. $3 621879
650 4 $a Automotive engineering. $3 2181195
650 4 $a Information science. $3 554358
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710 2 $a Rochester Institute of Technology. $b Electrical and Microelectronic Engineering. $3 3682563
773 0 $t Masters Abstracts International $g 84-03.
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