【论文阅读笔记】PACT：PArameterized Clipping Activation for Quantized Neural Networks
全文概括 本文目的是将 activation 和 weight 一起量化，其结果能量化 activation 和 weight 到 4-bit 大小，且准确度能和 full precision 媲美(在一系列流行的模型和数据集上)。 该方法是提出一个新的激活函数，即PACT(PArameterized Clipping Activation)，其作用在训练阶段，即该方案是从头开始训练的。
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相关文章
- 1. PACT: PARAMETERIZED CLIPPING ACTIVATION FOR QUANTIZED NEURAL NETWORKS
- 2. 【论文阅读笔记】-《Quantized Convolutional Neural Networks for Mobile Devices》
- 3. 【Quantized Convolutional Neural Networks for Mobile Devices】论文笔记
- 4. 论文笔记：Exploring Neural Networks with Activation Atlases
- 5. Global Sparse Momentum SGD for Pruning Very Deep Neural Networks 论文阅读笔记
- 6. 【论文阅读笔记】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 7. 【论文阅读笔记】Ristretto： Hardware-Oriented Approximation of Convolutional Neural Networks
- 8. 论文阅读：《Convolutional Neural Networks for Sentence Classification》
- 9. 论文阅读：Deep Neural Networks for Object Detection
- 10. 论文阅读-Fast Algorithms for Convolutional Neural Networks