| From high-level deep neural models to FPGAs H Sharma, J Park, D Mahajan, E Amaro, JK Kim, C Shao, A Mishra, ... 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture …, 2016 | 318 | 2016 |
| Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network H Sharma, J Park, N Suda, L Lai, B Chau, V Chandra, H Esmaeilzadeh 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture …, 2018 | 183 | 2018 |
| Tabla: A unified template-based framework for accelerating statistical machine learning D Mahajan, J Park, E Amaro, H Sharma, A Yazdanbakhsh, JK Kim, ... 2016 IEEE International Symposium on High Performance Computer Architecture …, 2016 | 120 | 2016 |
| Neural acceleration for gpu throughput processors A Yazdanbakhsh, J Park, H Sharma, P Lotfi-Kamran, H Esmaeilzadeh Proceedings of the 48th International Symposium on Microarchitecture, 482-493, 2015 | 78 | 2015 |
| Dnnweaver: From high-level deep network models to fpga acceleration H Sharma, J Park, E Amaro, B Thwaites, P Kotha, A Gupta, JK Kim, ... the Workshop on Cognitive Architectures, 2016 | 39 | 2016 |
| Scale-out acceleration for machine learning J Park, H Sharma, D Mahajan, JK Kim, P Olds, H Esmaeilzadeh Proceedings of the 50th Annual IEEE/ACM International Symposium on …, 2017 | 34 | 2017 |
| Bit-Parallel Vector Composability for Neural Acceleration S Ghodrati, H Sharma, C Young, NS Kim, H Esmaeilzadeh arXiv preprint arXiv:2004.05333, 2020 | 6 | 2020 |
| Processing artificial neural network weights AE Chalfin, H Sharma, TJ Olson US Patent 10,599,935, 2020 | 5 | 2020 |
| Mixed-signal charge-domain acceleration of deep neural networks through interleaved bit-partitioned arithmetic S Ghodrati, H Sharma, S Kinzer, A Yazdanbakhsh, J Park, NS Kim, ... Proceedings of the ACM International Conference on Parallel Architectures …, 2020 | 3 | 2020 |
| The impact of 3D stacking on GPU-accelerated deep neural networks: An experimental study W Wahby, T Sarvey, H Sharma, H Esmaeilzadeh, MS Bakir 2016 IEEE International 3D Systems Integration Conference (3DIC), 1-4, 2016 | 2 | 2016 |
| Accelerated deep learning for the edge-to-cloud continuum: A specialized full stack derived from algorithms H Sharma Georgia Institute of Technology, 2019 | 1 | 2019 |
| Planaria: Dynamic Architecture Fission for Spatial Multi-Tenant Acceleration of Deep Neural Networks S Ghodrati, BH Ahn, JK Kim, S Kinzer, BR Yatham, N Alla, H Sharma, ... 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture …, 2020 | | 2020 |
| Systems, apparatus, methods, and architecture for precision heterogeneity in accelerating neural networks for inference and training H Sharma, J Park US Patent App. 16/744,037, 2020 | | 2020 |
| Systems, apparatus, methods, and architectures for heterogeneous precision acceleration of quantized neural networks H Sharma, J Park US Patent App. 16/744,039, 2020 | | 2020 |
| Systems, apparatus, methods, and architectures for a neural network workflow to generate a hardware acceletator H Sharma, J Park US Patent App. 16/744,040, 2020 | | 2020 |
| Selecting encoding options S Pratapa, H Sharma, TJ Olson, AE Chalfin US Patent 10,559,093, 2020 | | 2020 |
| From Tensors to FPGAs: Accelerating Deep Learning H Sharma, J Park, B Samynathan, B Robatmili, S Mirkhani, ... Hot Chips: A Symposium on High Performance Chips, 2018 | | 2018 |
| Approximate Computing and Microfluidic Cooling for Enhanced Machine Learning H Sharma, W Wahby, T Sarvey, MS Bakir, H Esmailzadeh Workshop on Approximate Computing Across the System Stack (WAX), 2016 | | 2016 |
| Gamma-ray Spectroscopy of Spontaneous Ternary Fission of 252 Cf YN Kopatch, M Mutterer, P Jesinger, J Von Kalben, I Kojouharov, ... | | |
| TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning DMJPE Amaro, H Sharma, AYJK Kim, H Esmaeilzadeh | | |
Hadi EsmaeilzadehAssociate Professor; Computer Science and Engineering; University of California, San DiegoVerified email at eng.ucsd.edu
