Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149657
Title: Evaluating and optimizing neural network models with neuromorphic capable and non-capable hardware
Authors: Cheong, Gordon Chin Loong
Keywords: Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Cheong, 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. https://hdl.handle.net/10356/149657
Project: B2120-201
Abstract: The 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.
URI: https://hdl.handle.net/10356/149657
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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