Ganesh Venkatesh
Ganesh Venkatesh
Sr Research Scientist Manager
Zweryfikowany adres z meta.com
Cytowane przez
Cytowane przez
Mixed precision training
P Micikevicius, S Narang, J Alben, G Diamos, E Elsen, D Garcia, ...
arXiv preprint arXiv:1710.03740, 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
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
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
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
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
Accelerating Deep Convolutional Network via Low Precision and Sparsity
G Venkatesh, E Nurvitadhi, D Marr
Arxiv, 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
Unbounded page-based transactional memory
W Chuang, S Narayanasamy, G Venkatesh, J Sampson, ...
ACM SIGPLAN Notices 41 (11), 347-358, 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
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
Mixed precision training
S Narang, G Diamos, E Elsen, P Micikevicius, J Alben, D Garcia, ...
Proc. 6th Int. Conf. on Learning Representations (ICLR), 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
Mixed precision training. arXiv 2017
P Micikevicius, S Narang, J Alben, G Diamos, E Elsen, D Garcia, ...
arXiv preprint arXiv:1710.03740, 0
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
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
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
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
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
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
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