| 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 | 120 | 2017 |
| Loom: Exploiting weight and activation precisions to accelerate convolutional neural networks S Sharify, AD Lascorz, K Siu, P Judd, A Moshovos 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), 1-6, 2018 | 52 | 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 | 45* | 2019 |
| Laconic deep learning inference acceleration S Sharify, AD Lascorz, M Mahmoud, M Nikolic, K Siu, DM Stuart, Z Poulos, ... 2019 ACM/IEEE 46th Annual International Symposium on Computer Architecture …, 2019 | 27 | 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 | 20 | 2017 |
| 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 | 19 | 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 | 11* | 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 | 11 | 2017 |
| 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 | 8 | 2019 |
| Value-Based Deep-Learning Acceleration A Moshovos, J Albericio, P Judd, AD Lascorz, S Sharify, T Hetherington, ... IEEE Micro 38 (1), 41-55, 2018 | 5 | 2018 |
| 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 | 3 | 2018 |
| ACCELERATOR FOR DEEP NEURAL NETWORKS P Judd, J Albericio, A Delmas Lascorz, A Moshovos, S Sharify US Patent App. 16/504,275, 2020 | 1 | 2020 |
| 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 |
| Building an on-chip deep learning memory hierarchy brick by brick: late breaking results IE Vivancos, S Sharify, M Nikolic, C Bannon, M Mahmoud, AD Lascorz, ... Proceedings of the 57th ACM/EDAC/IEEE Design Automation Conference, 1-2, 2020 | | 2020 |
| Loom and Laconic: Hardware Accelerators for Deep Learning Algorithms S Sharifymoghaddam | | 2020 |
| 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 | | 2019 |
| Low-Swing Signaling for FPGA Power Reduction S Sharifymoghaddam, A Sheikholeslami Proceedings of the 2016 ACM/SIGDA International Symposium on Field …, 2016 | | 2016 |
| Low-Swing Signaling for FPGA Interconnect Power Reduction S Sharifymoghaddam | | 2015 |
| Ranganathan, Parthasarathy TA Khan, J Kim, JS Kim, J Kim, J Kim, MA Kim, NS Kim, Y Kim, A Kolli, ... | | |
Andreas MoshovosProfessor, Electrical and Computer Engineering, University of TorontoVerified email at eecg.toronto.edu
