Latent Weight-Based Pruning for Small Binary Neural Networks

Author:

Chen Tianen1,Anderson Noah2,Kim Younghyun1

Affiliation:

1. University of Wisconsin-Madison

2. Stanford University

Publisher

ACM

Reference20 articles.

1. Tom Bannink et al. 2021. Larq compute engine: Design benchmark and deploy state-of-the-art binarized neural networks. MLSys. Tom Bannink et al. 2021. Larq compute engine: Design benchmark and deploy state-of-the-art binarized neural networks. MLSys.

2. Yoshua Bengio et al. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432. Yoshua Bengio et al. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432.

3. Tianen Chen et al. 2022. SynthNet: A High-throughput yet Energy-efficient Combinational Logic Neural Network. ASP-DAC. Tianen Chen et al. 2022. SynthNet: A High-throughput yet Energy-efficient Combinational Logic Neural Network. ASP-DAC.

4. Xizi Chen et al. 2020. Tight compression: compressing CNN model tightly through unstructured pruning and simulated annealing based permutation. DAC. Xizi Chen et al. 2020. Tight compression: compressing CNN model tightly through unstructured pruning and simulated annealing based permutation. DAC.

5. Jonathan Frankle and Michael Carbin. 2018. The lottery ticket hypothesis: Finding sparse trainable neural networks. arXiv preprint arXiv:1803.03635. Jonathan Frankle and Michael Carbin. 2018. The lottery ticket hypothesis: Finding sparse trainable neural networks. arXiv preprint arXiv:1803.03635.

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