diff --git a/README.md b/README.md
index a5f35f764b1b86c0eb3982a10f8dd699af077880..fd5222655821e36fe194225a4d71a3b60b8a89d5 100644
--- a/README.md
+++ b/README.md
@@ -26,8 +26,42 @@ Paddle-Mobile是PaddlePaddle组织下的项目，是一个致力于嵌入式平
 
 - **ARM CPU**
 
-    arm cpu是paddle-mobile的主要支持方向，cpu的通用性一直是其优势。嵌入式深度学习，需要大量的cpu汇编实现。我们正在紧锣密鼓的编码，为的是能充分硬件的每一点加速能力。
-    arm cpu的优化工作还在进行中，现在使用了常规的cpu优化。在arm a73上paddle-mobile arm-v7现在单核运行一次mobilenet1.0是110+ms，显然这不是我们的最终目标，我们正在用大量的汇编改写，后续性能仍会有巨大提升空间, 目前只支持armv7, 未来我们也会支持armv8。
+|mobilenet arm v7|1线程|2线程|4线程|
+|------------|----|-----|-----|
+|麒麟970(ms)|108.180|63.935|37.545|
+|麒麟960(ms)|108.588|63.073|36.822|
+|高通845(ms)|85.952|48.890|28.641|
+|高通835(ms)|105.434|62.752|37.131|
+|||||
+|mobilenetssd arm v7|1线程|2线程|4线程|
+|麒麟970(ms)|212.686|127.205|77.485|
+|麒麟960(ms)|212.641|125.338|75.250|
+|高通845(ms)|182.863|95.671|56.857|
+|高通835(ms)|213.849|127.717|77.006|
+|||||
+|googlenet(v1) arm v7|1线程|2线程|4线程|
+|麒麟970(ms)|335.288|234.559|161.295|
+|麒麟960(ms)|354.443|232.642|157.815|
+|高通845(ms)|282.007|173.146|122.148|
+|高通835(ms)|341.250|233.354|158.554|
+|||||
+|squeezenet arm v7|1线程|2线程|4线程|
+|麒麟970(ms)|83.726|57.944|36.923|
+|麒麟960(ms)|85.835|55.762|36.496|
+|高通845(ms)|71.301|41.618|28.785|
+|高通835(ms)|82.407|56.176|36.455|
+|||||
+|yolo arm v7|1线程|2线程|4线程|
+|麒麟970(ms)|129.658|79.993|49.969|
+|麒麟960(ms)|130.208|78.791|48.390|
+|高通845(ms)|109.244|61.736|40.600|
+|高通835(ms)|130.402|80.863|50.359|
+
+    测试机型信息：
+    麒麟970:荣耀v10     (2.36GHz * 4 + 1.8GHz * 4)
+    麒麟960:华为mate9   (2.36GHz * 4 + 1.8GHz * 4)
+    骁龙835:小米6       (2.45GHz * 4 + 1.9GHz * 4)
+    骁龙845:OPPO FindX  (2.80GHz * 4 + 1.8GHz * 4)
     
 - **Mali GPU**
 
diff --git a/src/operators/kernel/central-arm-func/conv_transpose_arm_func.h b/src/operators/kernel/central-arm-func/conv_transpose_arm_func.h
index 51dac36aee0634ff0fb2b4bc1bcc45f663f84462..343e5f147644cc5bb86c2929d4bd35b44301c4cf 100644
--- a/src/operators/kernel/central-arm-func/conv_transpose_arm_func.h
+++ b/src/operators/kernel/central-arm-func/conv_transpose_arm_func.h
@@ -96,10 +96,6 @@ void ConvTransposeCompute(const ConvTransposeParam<CPU> &param) {
 
       math::matmul(filter_slice, true, in_slice, false, static_cast<P>(1.0),
                    &col_matrix, static_cast<P>(0.0));
-      //初始化out_slice的值为0
-      float *tmp = out_slice.data<float>();
-      int numel = out_slice.numel();
-      memset(static_cast<void *>(tmp), 0, sizeof(float) * numel);
       if (data_dim == 2U) {
         col2im(col, dilations, strides,
                std::vector<int>{paddings[0], paddings[1], paddings[0],
diff --git a/src/operators/math/im2col.cpp b/src/operators/math/im2col.cpp
index cb4d02f45c4229475d5c8ce896427a9aad228ef6..4065f7d9c4934bce8285ea99fe4f14c4e2cc990c 100644
--- a/src/operators/math/im2col.cpp
+++ b/src/operators/math/im2col.cpp
@@ -481,6 +481,7 @@ class Col2ImFunctor<ColFormat::kCFO, CPU, T> {
 
     T *im_data = im->data<T>();
     const T *col_data = col.data<T>();
+    memset(static_cast<void *>(im_data), 0, sizeof(T) * im->numel());
 
     for (int c = 0; c < channels_col; ++c) {
       int w_offset = c % filter_width;