CS231n Lecture 15 | Efficient Methods and Hardware for Deep Learning
Lecture 15主要从算法和硬件两个层面讲解了模型压缩和优化等问题，以实现深度学习模型的体积减少、参数数量缩减、计算量减少、计算加速等。 目前深度学习模型存在的几大问题。 降低能耗是很重要的，那么这些能量都消耗在哪了呢？ 上图可以看到存储访问的耗能比数学运算的耗能高两到三个数量级，因此，我们需要将算法和硬件联动设计来实现效能的提升。 我们将从以下四个方面来讲解： 在此之前，我们先来将一些硬件的
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相关文章
- 1. 【韩松博士毕业论文】Efficient methods and hardware for deep learning
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- 3. 【论文阅读】韩松《Efficient Methods And Hardware For Deep Learning》节选《Deep compression》
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