| Bit-pragmatic deep neural network computing J Albericio, A Delmás, P Judd, S Sharify, G O'Leary, R Genov, ... Proceedings of the 50th annual IEEE/ACM international symposium on …, 2017 | 262 | 2017 |
| Loom: Exploiting weight and activation precisions to accelerate convolutional neural networks S Sharify, AD Lascorz, K Siu, P Judd, A Moshovos Proceedings of the 55th Annual Design Automation Conference, 1-6, 2018 | 123 | 2018 |
| Bit-tactical: A software/hardware approach to exploiting value and bit sparsity in neural networks A Delmas Lascorz, P Judd, DM Stuart, Z Poulos, M Mahmoud, S Sharify, ... Proceedings of the Twenty-Fourth International Conference on Architectural …, 2019 | 117 | 2019 |
| Laconic deep learning inference acceleration S Sharify, AD Lascorz, M Mahmoud, M Nikolic, K Siu, DM Stuart, Z Poulos, ... Proceedings of the 46th International Symposium on Computer Architecture …, 2019 | 115 | 2019 |
| Cnvlutin2: Ineffectual-activation-and-weight-free deep neural network computing P Judd, A Delmas, S Sharify, A Moshovos arXiv preprint arXiv:1705.00125, 2017 | 79 | 2017 |
| Accelerator for deep neural networks P Judd, J Albericio, AD Lascorz, A Moshovos, S Sharifymoghaddam US Patent 11,610,100, 2023 | 78* | 2023 |
| 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 | 42 | 2019 |
| Exploiting Typical Values to Accelerate Deep Learning A Moshovos, J Albericio, P Judd, AD Lascorz, S Sharify, Z Poulos, ... Computer 51 (5), 18-30, 2018 | 39 | 2018 |
| Bit-tactical: Exploiting ineffectual computations in convolutional neural networks: Which, why, and how A Delmas, P Judd, DM Stuart, Z Poulos, M Mahmoud, S Sharify, M Nikolic, ... arXiv preprint arXiv:1803.03688, 2018 | 36* | 2018 |
| Dynamic stripes: Exploiting the dynamic precision requirements of activation values in neural networks A Delmas, P Judd, S Sharify, A Moshovos arXiv preprint arXiv:1706.00504, 2017 | 26 | 2017 |
| DPRed: Making typical activation and weight values matter in deep learning computing A Delmas, S Sharify, P Judd, K Siu, M Nikolic, A Moshovos arXiv preprint arXiv:1804.06732, 2018 | 21* | 2018 |
| Tartan: Accelerating fully-connected and convolutional layers in deep learning networks by exploiting numerical precision variability A Delmas, S Sharify, P Judd, A Moshovos arXiv preprint arXiv:1707.09068, 2017 | 20 | 2017 |
| Neural network accelerator A Moshovos, AD Lascorz, Z Poulos, DM Stuart, P Judd, S Sharify, ... US Patent App. 16/968,678, 2021 | 14 | 2021 |
| Boveda: Building an on-chip deep learning memory hierarchy brick by brick I Edo Vivancos, S Sharify, D Ly-Ma, A Abdelhadi, C Bannon, M Nikolic, ... Proceedings of Machine Learning and Systems 3, 1-20, 2021 | 9 | 2021 |
| Value-based deep-learning acceleration A Moshovos, J Albericio, P Judd, AD Lascorz, S Sharify, T Hetherington, ... IEEE Micro 38 (1), 41-55, 2018 | 7 | 2018 |
| Accelerating Image-Sensor-Based Deep Learning Applications M Mahmoud, DM Stuart, Z Poulos, AD Lascorz, P Judd, S Sharify, ... IEEE Micro 39 (5), 26-35, 2019 | 1 | 2019 |
| Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning A Moshovos, J Albericio, P Judd, A Delmas, S Sharify, M Mahmoud, ... 2018 16th IEEE International New Circuits and Systems Conference (NEWCAS …, 2018 | 1 | 2018 |
| Self-Selected Attention Span for Accelerating Large Language Model Inference T Jin, Z Xu, S Sharify, X Wang arXiv preprint arXiv:2404.09336, 2024 | | 2024 |
| Mixed-Precision Quantization with Cross-Layer Dependencies Z Deng, X Wang, S Sharify, M Orshansky arXiv preprint arXiv:2307.05657, 2023 | | 2023 |
| Accelerator for deep neural networks P Judd, J Albericio, AD Lascorz, A Moshovos, S Sharify US Patent App. 18/108,292, 2023 | | 2023 |
Andreas MoshovosProfessor, Electrical and Computer Engineering, University of TorontoVerified email at eecg.toronto.edu
