RETROSPECTIVE: Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing

By Jorge Albericio, Patrick Judd, Tayler H. Hetherington, Tor M. Aamodt, Natalie D. Enright Jerger, Andreas Moshovos. 2023

Subfields: Model Architectures, 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 Model Architectures

1. Shiji Song
2. Yulin Wang
3. Gao Huang
4. Bashir I. Morshed
5. Honghui Wang
6. Syed Kamrul Islam
7. Le Yang
8. Jianlin Cheng

In Efficiency & Compression

1. Shiji Song
2. Gao Huang
3. Yulin Wang
4. Le Yang
5. Yizeng Han
6. Honghui Wang
7. Bashir I. Morshed
8. Syed Kamrul Islam

Citations in the corpus

• Dynamic Neural Networks: A Survey (2021, background): “Many input-dependent operations, including pixel-level dynamic computation [114], [275], [276], adaptive channel pruning [277], [278] and early exiting [279], have also been tailored together with hardware for further improving their practical efficiency.”
• Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review (2023, methodology): “Cnvlutin [245] uses the CSR scheme for compressed activation without exploiting the weight sparsity.”