Spectral domain convolutional neural network optimized for computational workload and memory access cost

• Main Author: Rizvi, Shahriyar Masud
• Format: Thesis
• Language: English
• Published: 2023
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/102817/1/ShahriyarMasudRizviPSKE2023.pdf.pdf; http://eprints.utm.my/id/eprint/102817/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:152199
• Institution: Universiti Teknologi Malaysia

Conventional convolutional neural networks (CNNs), which are realized in the spatial domain, present a high computationalworkload and memory access cost (CMC). Spectral domain CNNs (SpCNNs) offer a computationally efficient approach for performing CNN training and inference. State-of-the-art SpCNNs...