Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCheong, Gordon Chin Loongen_US
dc.identifier.citationCheong, G. C. L. (2021). Evaluating and optimizing neural network models with neuromorphic capable and non-capable hardware. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractThe main objective of this project is to evaluate and optimize Spiking Neural Network with the Novena Chip to achieve high accuracy, low processing time, and low power consumption. The integrated pair (Spiking Neural Network with Novena) will be benchmarked against other conventional convolution neural networks running on non-neuromorphic hardware. The conventional convolution neural networks used in this paper will be ResNet-50, Inception V4, and MobileNet. The non-neuromorphic hardware used will be Nvidia’s NanoJetson, Raspberry Pi 4B with Intel’s Neural Compute Stick 2, Raspberry Pi 4B with Coral’s USB Accelerator, and ASUS Tinker Edge T. All experiments will be making use of the same dataset for both visual and audio component.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleEvaluating and optimizing neural network models with neuromorphic capable and non-capable hardwareen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLeong Wei Linen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.organizationAgency for Science, Technology and Researchen_US
dc.contributor.supervisor2Jiang Wenyuen_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
  Restricted Access
1.6 MBAdobe PDFView/Open

Page view(s)

Updated on Jun 28, 2022


Updated on Jun 28, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.