Weighted-Entropy-based Quantization for Deep Neural Networks
本文提出了一种综合考虑权重的重要性和频率来进行量化的方式。它与其它量化方式的对比： 作者首先定义了被称为权重熵的变量： 这里Pn代表了权重的频率，In代表了它的重要性。作者设定了n个量化簇(cluster)，i(n,m)是计算第n个簇的第m个权重的重要性的函数，作者这里简单地选择了平方函数。 上图是权重量化的伪代码：首先用平方函数计算每个权重的重要性，然后从小到大对其排序，并将它们均匀分为n个间隔
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相关文章
- 1. Weighted-Entropy-based Quantization for Deep Neural Networks
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- 4. Deep Neural Networks for Object Detection
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- 8. deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi
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