| Gobo: Quantizing attention-based nlp models for low latency and energy efficient inference AH Zadeh, I Edo, OM Awad, A Moshovos 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture …, 2020 | 165 | 2020 |
| Tensordash: Exploiting sparsity to accelerate deep neural network training M Mahmoud, I Edo, AH Zadeh, OM Awad, G Pekhimenko, J Albericio, ... 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture …, 2020 | 81 | 2020 |
| Shapeshifter: Enabling fine-grain data width adaptation in deep learning AD Lascorz, S Sharify, I Edo, DM Stuart, OM Awad, P Judd, M Mahmoud, ... Proceedings of the 52nd Annual IEEE/ACM International Symposium on …, 2019 | 46 | 2019 |
| Security implications of intentional capacitive crosstalk C Kison, OM Awad, M Fyrbiak, C Paar IEEE Transactions on Information Forensics and Security 14 (12), 3246-3258, 2019 | 36 | 2019 |
| Bitpruning: Learning bitlengths for aggressive and accurate quantization M Nikolić, GB Hacene, C Bannon, AD Lascorz, M Courbariaux, OM Awad, ... 2024 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2024 | 28 | 2024 |
| FPRaker: A processing element for accelerating neural network training OM Awad, M Mahmoud, I Edo, AH Zadeh, C Bannon, A Jayarajan, ... MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture …, 2021 | 18 | 2021 |
| Compressing pre-trained language models using progressive low rank decomposition H Hajimolahoseini, M Rezagholizadeh, V Partovinia, M Tahaei, OM Awad, ... Advances in Neural Information Processing Systems, 2021 | 10 | 2021 |
| GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values F Javadi, W Ahmed, H Hajimolahoseini, F Ataiefard, M Hassanpour, ... arXiv preprint arXiv:2311.03426, 2023 | 3 | 2023 |
| SkipViT: Speeding Up Vision Transformers with a Token-Level Skip Connection F Ataiefard, W Ahmed, H Hajimolahoseini, S Asani, F Javadi, ... arXiv preprint arXiv:2401.15293, 2024 | 2 | 2024 |
| SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling H Hajimolahoseini, OM Awad, W Ahmed, A Wen, S Asani, M Hassanpour, ... arXiv preprint arXiv:2311.15134, 2023 | 2 | 2023 |
| Improving Resnet-9 Generalization Trained on Small Datasets OM Awad, H Hajimolahoseini, M Lim, G Gosal, W Ahmed, Y Liu, G Deng arXiv preprint arXiv:2309.03965, 2023 | 1 | 2023 |
| Quantization for neural network computation A Moshovos, AH Zadeh, IE Vivancos, OM Awad US Patent App. 17/130,690, 2022 | 1 | 2022 |
| GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference A Hadi Zadeh, I Edo, OM Awad, A Moshovos arXiv e-prints, arXiv: 2005.03842, 2020 | 1 | 2020 |
| Tensordash: Exploiting sparsity to accelerate deep neural network training and inference M Mahmoud, IE Vivancos, O Awad, AH Zadeh, G Pekhimenko, J Albericio, ... Arxiv preprint cs. AR, 0 | 1 | |
| Quantization for neural network computation A Moshovos, AH Zadeh, IE Vivancos, OM Awad US Patent App. 18/026,927, 2023 | | 2023 |
| System and method for accelerating training of deep learning networks OM Awad, M Mahmoud, A Moshovos US Patent App. 18/005,717, 2023 | | 2023 |
| cuSCNN: an Efficient CUDA Implementation of Sparse CNNs MA Elgammal, OM Awad, IE Vivancos, A Moshovos, V Betz Proceedings of the 13th International Symposium on Highly Efficient …, 2023 | | 2023 |
| FPRaker: Exploiting Fine-grain Sparsity to Accelerate Neural Network Training OAMA Mohamed Awad | | 2020 |
Andreas MoshovosProfessor, Electrical and Computer Engineering, University of Toronto在 eecg.toronto.edu 的电子邮件经过验证
