论文笔记：Quantizing deep convolutional networks for efficient inference: A whitepaper
地址：https://arxiv.org/abs/1806.08342 这篇白皮书统一介绍了当前将网络进行定点化的方法。下面将看到的重点记录下来。 为了降低深度神经网络的计算量以及带宽（内存占用量），一种简单可行的方式就是将浮点网络（网络权重都是float类型数值）转换成定点网络（网络权重都是或者部分是int8类型）具体来说，这么做的优势在于： 不用改变网络结构，不用重新训练 单个int8定点数的
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相关文章
- 1. 论文总结：Quantizing deep convolutional networks for efficient inference: A whitepaper
- 2. 【论文阅读笔记】ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
- 3. Pruning Convolutional Neural Networks For Resource Efficient Inference
- 4. 【论文笔记】Resolution Adaptive Networks for Efficient Inference （CVPR2020）
- 5. [论文笔记]Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- 6. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications论文笔记
- 7. 【论文阅读笔记】MobileNets Efficient Convolutional Neural Networks for Mobile Vision Applications
- 8. 论文笔记——Multi-Scale Dense Convolutional Networks for Efficient Prediction
- 9. 论文阅读——ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
- 10. 论文笔记：An Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs