A joint energy and latency framework for transfer learning over 5G industrial edge networks B Yang, O Fagbohungbe, X Cao, C Yuen, L Qian, D Niyato, Y Zhang IEEE Transactions on Industrial Informatics 18 (1), 531-541, 2021 | 36 | 2021 |
Efficient privacy preserving edge intelligent computing framework for image classification in iot O Fagbohungbe, SR Reza, X Dong, L Qian IEEE Transactions on Emerging Topics in Computational Intelligence 6 (4 …, 2021 | 31 | 2021 |
Using the IBM analog in-memory hardware acceleration kit for neural network training and inference M Le Gallo, C Lammie, J Büchel, F Carta, O Fagbohungbe, C Mackin, ... APL Machine Learning 1 (4), 2023 | 16 | 2023 |
Benchmarking inference performance of deep learning models on analog devices O Fagbohungbe, L Qian 2021 International Joint Conference on Neural Networks (IJCNN), 1-9, 2021 | 16 | 2021 |
Fast and robust analog in-memory deep neural network training MJ Rasch, F Carta, O Fagbohungbe, T Gokmen Nature Communications 15 (1), 7133, 2024 | 7* | 2024 |
The Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models O Fagbohungbe, L Qian IEEE Access 10, 127728-127741, 2022 | 5 | 2022 |
Impact of learning rate on noise resistant property of deep learning models O Fagbohungbe, L Qian Proceedings of the Future Technologies Conference, 14-30, 2023 | 3 | 2023 |
Demonstration of transfer learning using 14 nm technology analog ReRAM array FF Athena, O Fagbohungbe, N Gong, MJ Rasch, J Penaloza, SC Seo, ... Frontiers in Electronics 4, 1331280, 2024 | | 2024 |
Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models O Fagbohungbe, L Qian 2022 International Joint Conference on Neural Networks (IJCNN), 1-9, 2022 | | 2022 |