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SCENIC: An Area and Energy-Efficient CNN-based Hardware Acc..:

, In: 2022 35th International Conference on VLSI Design and 2022 21st International Conference on Embedded Systems (VLSID),

Naidu, Bodepu Sai Tirumala ; Biswas, Shreya ; Chatterjee, Rounak... - p. 168-173 , 2022

Link: https://doi.org/10.1109/VLSID2022.2022.00042

RT 
T1 <cTitle>2022 35th International Conference on VLSI Design and 2022 21st International Conference on Embedded Systems (VLSID)</cTitle>: T1 <Title>SCENIC: An Area and Energy-Efficient CNN-based Hardware Accelerator for Discernable Classification of Brain Pathologies using MRI</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-9885938&Exemplar=1&LAN=DE
				
A1 Naidu, Bodepu Sai Tirumala
A1 Biswas, Shreya
A1 Chatterjee, Rounak
A1 Mandal, Sayak
A1 Pratihar, Srijan
A1 Chatterjee, Ayan
A1 Raha, Arnab
A1 Mukherjee, Amitava
A1 Paluh, Janet
YR 2022
SN 2380-6923
K1 Wavelet transforms
K1 Visualization
K1 Magnetic resonance imaging
K1 Biological system modeling
K1 Neuropathology
K1 Very large scale integration
K1 Software
K1 Brain tumor
K1 biomedical image
K1 discrete wavelet transform
K1 hardware accelerator
K1 neural networks
K1 MRI
K1 pathology classification
SP 168
OP 173
LK http://dx.doi.org/https://doi.org/10.1109/VLSID2022.2022.00042
DO https://doi.org/10.1109/VLSID2022.2022.00042
SF ELIB - SuUB Bremen

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