翻譯鏈接
可以說是很詳盡了
Forward and Backward Information Retention for Accurate Binary Neural Networks 文章目錄Forward and Backward Information
文章目錄Training Quantized Neural Networks with a Full-precision Auxiliary ModuleIntroductionMethodExperiment Training
文章目錄motivationMethodMaximizing Entropy with Balanced Binary WeightsReconstructing Information Flow with Gated Resid
本文主要講述 quantized 版本的矩陣乘法的計算流程，矩陣乘如下： C=A×BC=A\times BC=A×B 其中 AAA 爲 uint8 類型， BBB 爲 int8 類型，C爲 uint8 類型。其詳細計算流程爲u8
模型量化作爲一種能夠有效減少模型大小，加速深度學習推理的優化技術，已經得到了學術界和工業界的廣泛研究和應用。模型量化有 8/4/2/1 bit等，本文主要討論目前相對比較成熟的 8-bit 低精度推理。 通過這篇文章你可以學習到以下內容：
Noise injection and clamping estimation for neural network quantization 文章目錄Noise injection and clamping estimation
Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks 文章目錄Ristretto: Hardware-Oriented Approx
Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1，+1} 文章目錄Multi-Precision Quantized Neura
Back to Simplicity: How to Train Accurate BNNs from Scratch? 文章目錄Back to Simplicity: How to Train Accurate BNNs fro
PACT: parameterized clipping activation for quantization neural networks 文章目錄PACT: parameterized clipping activatio
Towards Accurate Binary Convolutional Neural Network 文章目錄Towards Accurate Binary Convolutional Neural NetworkIntrod
