Update README for our arxiv eprint

README.md

@@ -37,7 +37,7 @@ dabnn_3x3_256                    975123 ns     969810 ns        691     <--- inp
 dabnn_3x3_256_s2                 268310 ns     267712 ns       2618     <--- input: 14*14*256, kernel: 256*3*3*256, output: 7*7*256, padding: 1, stride: 2
 dabnn_3x3_512                   1281832 ns    1253921 ns        588     <--- input:  7* 7*512, kernel: 512*3*3*512, output:  7* 7*512, padding: 1
 dabnn_bireal18_imagenet        61920154 ns   61339185 ns         10     <--- Bi-Real Net 18, 56.4% top-1 on ImageNet
-dabnn_bireal18_imagenet_stem   43294019 ns   41401923 ns         14     <--- Bi-Real Net 18 with stem module (The network structure will be described in detail in the coming paper), 56.4% top-1 on ImageNet
+dabnn_bireal18_imagenet_stem   43294019 ns   41401923 ns         14     <--- Bi-Real Net 18 with stem module (The network structure is described in detail in our paper https://arxiv.org/abs/1908.05858), 56.4% top-1 on ImageNet
 ```
 
 The following is the comparison between our dabnn and [Caffe](http://caffe.berkeleyvision.org) (full precision), [TensorFlow Lite](https://www.tensorflow.org/lite) (full precision) and [BMXNet](https://github.com/hpi-xnor/BMXNet) (binary). We surprisingly observe that BMXNet is even slower than the full precision TensorFlow Lite. It suggests that the potential of binary neural networks is far from exploited until our dabnn is published.
@@ -64,7 +64,7 @@ We publish two pretrained binary neural network models based on [Bi-Real Net](ht
 
 * Bi-Real Net 18, 56.4% top-1 on ImageNet, 61.3ms/image on Google Pixel 1 (single thread). [[dabnn](https://drive.google.com/uc?export=download&id=1Oau5CtFR9nWXmlBBU47Jg5ypMiIEMtvo)] [[ONNX](https://drive.google.com/uc?export=download&id=1Xp3HB51H6Nhl6e555ieJubVutQake5sR)]
 
-* Bi-Real Net 18 with Stem Module, 56.4% top-1 on ImageNet, 43.2ms/image on Google Pixel 1 (single thread). The detailed network structure will be described in the coming paper. [[dabnn](https://drive.google.com/uc?export=download&id=1ArsirMdbtJ9lvHSjc1hkQ7dIXDKh-D1t)] [[ONNX](https://drive.google.com/uc?export=download&id=1zu48CFptAGZ91IDCBPJSPM0bxDuPm9HS)]
+* Bi-Real Net 18 with Stem Module, 56.4% top-1 on ImageNet, 43.2ms/image on Google Pixel 1 (single thread). The detailed network structure is described our paper https://arxiv.org/abs/1908.05858. [[dabnn](https://drive.google.com/uc?export=download&id=1ArsirMdbtJ9lvHSjc1hkQ7dIXDKh-D1t)] [[ONNX](https://drive.google.com/uc?export=download&id=1zu48CFptAGZ91IDCBPJSPM0bxDuPm9HS)]
 
 ## Implementation Details
 
@@ -76,6 +76,17 @@ We publish two pretrained binary neural network models based on [Bi-Real Net](ht
 
 Android app demo: https://github.com/JDAI-CV/dabnn-example
 
-## License
+## License and Citation
 
 [BSD 3 Clause](LICENSE)
+
+Please cite daBNN in your publications if it helps your research:
+
+```
+@misc{zhang2019dabnn,
+  Author = {Jianhao Zhang and Yingwei Pan and Ting Yao and He Zhao and Tao Mei},
+  Title = {daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices},
+  Year = {2019},
+  Eprint = {arXiv:1908.05858},
+}
+```

README_CN.md

@@ -39,7 +39,7 @@ dabnn_3x3_256                    975123 ns     969810 ns        691     <--- inp
 dabnn_3x3_256_s2                 268310 ns     267712 ns       2618     <--- input: 14*14*256, kernel: 256*3*3*256, output: 7*7*256, padding: 1, stride: 2
 dabnn_3x3_512                   1281832 ns    1253921 ns        588     <--- input:  7* 7*512, kernel: 512*3*3*512, output:  7* 7*512, padding: 1
 dabnn_bireal18_imagenet        61920154 ns   61339185 ns         10     <--- Bi-Real Net 18, ImageNet top-1 为 56.4%
-dabnn_bireal18_imagenet_stem   43294019 ns   41401923 ns         14     <--- 带有 stem 模块的 Bi-Real Net 18 (将在论文中描述), ImageNet top-1 为 56.4%
+dabnn_bireal18_imagenet_stem   43294019 ns   41401923 ns         14     <--- 带有 stem 模块的 Bi-Real Net 18 (在我们的 paper https://arxiv.org/abs/1908.05858 中有描述), ImageNet top-1 为 56.4%
 ```
 
 在 Google Pixel 1 上与 [Caffe](http://caffe.berkeleyvision.org)（全精度）, [TensorFlow Lite](https://www.tensorflow.org/lite)（全精度）和 [BMXNet](https://github.com/hpi-xnor/BMXNet)（二值）的对比如下。我们很惊讶的发现现有的二值 inference 框架 BMXNet 甚至比全精度的 TensorFlow Lite 还要慢，这表明，直到 dabnn 推出之前，二值网络的潜力都远远没有被挖掘出来。
@@ -66,7 +66,7 @@ dabnn_bireal18_imagenet_stem   43294019 ns   41401923 ns         14     <--- 带
 
 * Bi-Real Net 18, ImageNet top-1 为 56.4%, 在 Google Pixel 1 上的速度为 61.8ms/image （单线程）。[[dabnn](https://drive.google.com/uc?export=download&id=1Oau5CtFR9nWXmlBBU47Jg5ypMiIEMtvo)] [[ONNX](https://drive.google.com/uc?export=download&id=1Xp3HB51H6Nhl6e555ieJubVutQake5sR)]
 
-* Bi-Real Net 18 with Stem Module, ImageNet top-1 为 56.4%, 在 Google Pixel 1 上的速度为 43.2ms/image （单线程）。详细的网络结构将在论文中描述。 [[dabnn](https://drive.google.com/uc?export=download&id=1ArsirMdbtJ9lvHSjc1hkQ7dIXDKh-D1t)] [[ONNX](https://drive.google.com/uc?export=download&id=1zu48CFptAGZ91IDCBPJSPM0bxDuPm9HS)]
+* Bi-Real Net 18 with Stem Module, ImageNet top-1 为 56.4%, 在 Google Pixel 1 上的速度为 43.2ms/image （单线程）。详细的网络结构在我们的 paper https://arxiv.org/abs/1908.05858 中有描述。 [[dabnn](https://drive.google.com/uc?export=download&id=1ArsirMdbtJ9lvHSjc1hkQ7dIXDKh-D1t)] [[ONNX](https://drive.google.com/uc?export=download&id=1zu48CFptAGZ91IDCBPJSPM0bxDuPm9HS)]
 
 ## 技术细节
 
@@ -78,6 +78,17 @@ dabnn_bireal18_imagenet_stem   43294019 ns   41401923 ns         14     <--- 带
 
 Android app demo: https://github.com/JDAI-CV/dabnn-example
 
-## 开源许可
+## 开源许可和引用
 
 [BSD 3 Clause](LICENSE)
+
+如果使用了 dabnn，可以按如下格式引用：
+
+```
+@misc{zhang2019dabnn,
+  Author = {Jianhao Zhang and Yingwei Pan and Ting Yao and He Zhao and Tao Mei},
+  Title = {daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices},
+  Year = {2019},
+  Eprint = {arXiv:1908.05858},
+}
+```