Learning Structured Sparsity in Deep Neural Networks
1. 文章介紹 DNN，尤其是CNN，已經通過從大量數據中的大規模學習在計算機視覺領域取得了巨大的成功，但是這樣的大模型部署是有問題的。爲了減少計算成本，許多研究方法被用來壓縮DNN的大小，包括稀疏正則化、連接剪枝和低秩近似，稀疏正則化和連接剪枝通常會得到一個非結構化的隨機連接關係，因此不規則的內存訪問不利於硬件加速，Figure 1展示了通過L1正則化對AlexNet每一層的非結構稀疏化。與原始
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相關文章
- 1. 神經網絡壓縮（4） Learning Structured Sparsity in Deep Neural Networks
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