BinaryConnect: Training Deep Neural Networks with binary weights during propagations 論文筆記
0 摘要 深度神經網絡在大量任務中取得了最先進的成果。GPU因爲其更快的計算速度，幫助深度網絡實現了這些突破。未來，在訓練和測試時更快的計算速度對於進一步發展，以及能夠在低功耗設備上的消費級別的應用可能至關重要。因此，對深度學習專用硬件的研究和開發展開了新的熱潮。二值權重，即僅限於兩個可能值（例如-1或1）的權重，通過用簡單累加代替許多乘法—累加操作，爲專用DL硬件帶來巨大便利。因爲乘法器
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相關文章
- 1. BinaryConnect： Training Deep Neural Networks with binary weights during propagations
- 2. 【論文閱讀筆記】Ristretto： Hardware-Oriented Approximation of Convolutional Neural Networks
- 3. 論文筆記 - Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
- 4. 【論文筆記】Training Very Deep Networks - Highway Networks
- 5. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】論文筆記
- 6. [論文筆記] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
- 7. Binarized Neural Networks:Training Deep Neural Networks with Weights and Activations -1,1
- 8. Balanced Binary Neural Networks With Gated Residual
- 9. AlexNet論文(ImageNet Classification with Deep Convolutional Neural Networks)學習筆記
- 10. TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS