BinaryConnect: Training Deep Neural Networks with binary weights during propagations 论文笔记
0 摘要 深度神经网络在大量任务中取得了最先进的成果。GPU因为其更快的计算速度，帮助深度网络实现了这些突破。未来，在训练和测试时更快的计算速度对于进一步发展，以及能够在低功耗设备上的消费级别的应用可能至关重要。因此，对深度学习专用硬件的研究和开发展开了新的热潮。二值权重，即仅限于两个可能值（例如-1或1）的权重，通过用简单累加代替许多乘法—累加操作，为专用DL硬件带来巨大便利。因为乘法器
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相关文章
- 1. BinaryConnect： Training Deep Neural Networks with binary weights during propagations
- 2. 【论文阅读笔记】Ristretto： Hardware-Oriented Approximation of Convolutional Neural Networks
- 3. 论文笔记 - Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
- 4. 【论文笔记】Training Very Deep Networks - Highway Networks
- 5. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
- 6. [论文笔记] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
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- 8. Balanced Binary Neural Networks With Gated Residual
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