Accelerating deep convolutional networks using low-precision and sparsity
（這篇blog不涉及文中所探討的dLAC設計的內容） 這篇文章旨在不影響其準確率的情況下提高deep CNN的計算效率。作者採用了兩種方法：1.使用2-bit代替原來的full precision進行訓練和inference；2.跳過過於zero value的計算。 1 low-precision deep CNN 作者使用了先前研究者提出的ternary network的框架，使用2-bit來訓
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相關文章
- 1. Learning Structured Sparsity in Deep Neural Networks
- 2. 【DDFD】《Multi-view Face Detection Using Deep Convolutional Neural Networks》
- 3. Channel Pruning for Accelerating Very Deep Neural Networks
- 4. Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 5. （六）6.4 Neurons Networks Autoencoders and Sparsity
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- 7. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
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- 9. 《Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks》論文筆記
- 10. Convolutional Neural Networks----Deep Convolutional Models:case studies