Mixed precision training P Micikevicius, S Narang, J Alben, G Diamos, E Elsen, D Garcia, ... arXiv preprint arXiv:1710.03740, 2017 | 1445 | 2017 |
Conservation cores: reducing the energy of mature computations G Venkatesh, J Sampson, N Goulding, S Garcia, V Bryksin, ... ACM SIGARCH Computer Architecture News 38 (1), 205-218, 2010 | 664 | 2010 |
Can FPGAs beat GPUs in accelerating next-generation deep neural networks? E Nurvitadhi, G Venkatesh, J Sim, D Marr, R Huang, J Ong Gee Hock, ... Proceedings of the 2017 ACM/SIGDA international symposium on field …, 2017 | 535 | 2017 |
Accelerating binarized neural networks: Comparison of FPGA, CPU, GPU, and ASIC E Nurvitadhi, D Sheffield, J Sim, A Mishra, G Venkatesh, D Marr 2016 International Conference on Field-Programmable Technology (FPT), 77-84, 2016 | 369 | 2016 |
The GreenDroid mobile application processor: An architecture for silicon's dark future N Goulding-Hotta, J Sampson, G Venkatesh, S Garcia, J Auricchio, ... IEEE Micro 31 (2), 86-95, 2011 | 214 | 2011 |
QsCores: Trading dark silicon for scalable energy efficiency with quasi-specific cores G Venkatesh, J Sampson, N Goulding-Hotta, SK Venkata, MB Taylor, ... Proceedings of the 44th Annual IEEE/ACM International Symposium on …, 2011 | 162 | 2011 |
Accelerating Deep Convolutional Network via Low Precision and Sparsity G Venkatesh, E Nurvitadhi, D Marr Arxiv, 2016 | 156* | 2016 |
Runnemede: An architecture for ubiquitous high-performance computing NP Carter, A Agrawal, S Borkar, R Cledat, H David, D Dunning, J Fryman, ... High Performance Computer Architecture (HPCA2013), 2013 IEEE 19th …, 2013 | 139 | 2013 |
Unbounded page-based transactional memory W Chuang, S Narayanasamy, G Venkatesh, J Sampson, ... ACM SIGPLAN Notices 41 (11), 347-358, 2006 | 131 | 2006 |
Accelerating sparse deep neural networks A Mishra, JA Latorre, J Pool, D Stosic, D Stosic, G Venkatesh, C Yu, ... arXiv preprint arXiv:2104.08378, 2021 | 114 | 2021 |
Efficient complex operators for irregular codes J Sampson, G Venkatesh, N Goulding-Hotta, S Garcia, S Swanson, ... High Performance Computer Architecture (HPCA), 2011 IEEE 17th International …, 2011 | 56 | 2011 |
Mixed precision training S Narang, G Diamos, E Elsen, P Micikevicius, J Alben, D Garcia, ... Proc. 6th Int. Conf. on Learning Representations (ICLR), 2018 | 53 | 2018 |
GreenDroid: A mobile application processor for a future of dark silicon N Goulding, J Sampson, G Venkatesh, S Garcia, J Auricchio, J Babb, ... Hot Chips 22, 2010 | 50 | 2010 |
Mixed precision training. arXiv 2017 P Micikevicius, S Narang, J Alben, G Diamos, E Elsen, D Garcia, ... arXiv preprint arXiv:1710.03740, 0 | 39 | |
Can temporal information help with contrastive self-supervised learning? Y Bai, H Fan, I Misra, G Venkatesh, Y Lu, Y Zhou, Q Yu, V Chandra, ... arXiv preprint arXiv:2011.13046, 2020 | 36 | 2020 |
Fine-grained accelerators for sparse machine learning workloads AK Mishra, E Nurvitadhi, G Venkatesh, J Pearce, D Marr 2017 22nd Asia and South Pacific design automation conference (ASP-DAC), 635-640, 2017 | 34 | 2017 |
Learning dynamic network using a reuse gate function in semi-supervised video object segmentation H Park, J Yoo, S Jeong, G Venkatesh, N Kwak Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 29 | 2021 |
Hardware accelerator for analytics of sparse data E Nurvitadhi, A Mishra, Y Wang, G Venkatesh, D Marr 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2016 | 28 | 2016 |
Temporally consistent online depth estimation in dynamic scenes Z Li, W Ye, D Wang, FX Creighton, RH Taylor, G Venkatesh, M Unberath Proceedings of the IEEE/CVF winter conference on applications of computer …, 2023 | 13 | 2023 |
An Evaluation of Selective Depipelining for FPGA-based Energy-Reducing Irregular Code Coprocessors J Sampson, M Arora, N Goulding-Hotta, G Venkatesh, J Babb, V Bhatt, ... Field Programmable Logic and Applications (FPL), 2011 International …, 2011 | 13 | 2011 |