Optimizing the efficiency of deep learning through accelerator virtualization

By M. Gschwind, Tim Kaldewey, David K. Tam. 2017

11 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Efficiency & Compression

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Carole-Jean Wu
2. Michael G. Rabbat
3. Amany M. Sarhan
4. Kim M. Hazelwood
5. M. Arafa
6. Bilge Acun
7. Hsien-Hsin S. Lee
8. Salvatore Candido

In Electrical and Electronic Engineering

1. Udit Gupta
2. Carole-Jean Wu
3. Michael G. Rabbat
4. Newsha Ardalani
5. Ramya Raghavendra
6. Young Geun Kim
7. Alexander Olshevsky
8. Angelia Nedić

In Efficiency & Compression

1. Michael G. Rabbat
2. Udit Gupta
3. Carole-Jean Wu
4. Myle Ott

Citations in the corpus

• Sustainable AI: Environmental Implications, Challenges and Opportunities (2021, background): “Multi-tenancy for AI accelerators is gaining traction as an effective way to improve resource utilization, thereby amortizing the upfront embodied carbon footprint of customized system hardware for AI at the expense of potential operational carbon footprint increase [73], [74], [75], [76], [77].”
• A modified Adam algorithm for deep neural network optimization (2023, background): “They enable us to train deep neural networks much faster than before [2, 3].”