diff --git a/mobile/src/fpga/V2/api.cpp b/mobile/src/fpga/V2/api.cpp
index f1d19364f89cfa7118397ab7f33db66c3a78785d..f39d012e08c124feacbd72fa2879e60b352c2785 100644
--- a/mobile/src/fpga/V2/api.cpp
+++ b/mobile/src/fpga/V2/api.cpp
@@ -359,7 +359,7 @@ void expand_conv_arg(ConvArgs *arg) {
   if (((res_win % 2) != 0) && (res_win != 1)) {
     res_win = res_win - 1;
   }
-  PADDLE_MOBILE_ENFORCE(res_win >= 2, "window too bigger than fpga volume");
+  //  PADDLE_MOBILE_ENFORCE(res_win >= 2, "window too bigger than fpga volume");
   res_fit = res_win;
 
   auto block_num = (output_width + res_fit - 1) / res_fit;
@@ -885,7 +885,7 @@ void fill_dwconv_arg(struct DWconvArgs *arg, framework::Tensor *input,
                      int padding_h, int padding_w, float *bias_ptr) {
   auto filter_ptr = filter->data<int16_t>();
   auto input_ptr = input->data<int8_t>();
-  auto output_ptr = out->mutable_data<int8_t>();
+  auto output_ptr = out->data<int8_t>();
   arg->sub_conv_num = 1;
   arg->relu_enabled = relu_enabled;
   // arg->output.activation.activation_type = activation_enable;
diff --git a/mobile/src/fpga/V2/bias_scale.cpp b/mobile/src/fpga/V2/bias_scale.cpp
index ca93fe17ca61e13022e7c86137f8ab3e4b6ed6d9..e04604c587dc6ba1ea29c310a502aba8f8b7b153 100644
--- a/mobile/src/fpga/V2/bias_scale.cpp
+++ b/mobile/src/fpga/V2/bias_scale.cpp
@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "fpga/V2/bias_scale.h"
 #include <memory.h>
+#include <math.h>
 #include "fpga/common/fpga_common.h"
 
 namespace paddle_mobile {
@@ -55,10 +56,22 @@ void align_element(float **data_in, int num_per_div_before_alignment, int num) {
   *data_in = ptr_aligned;
 }
 
+void fixed_scale_bias_new(void*data_in, int data_len) {
+    int* data_tmp = static_cast<int*>(data_in);
+    for (int idx = 0; idx < data_len/2; ++idx) {
+        float tmp = (static_cast<float*>(data_in))[idx];
+        data_tmp[idx] = static_cast<int>(round(tmp*pow(2.0, 23.0)));
+        tmp = (static_cast<float*>(data_in))[idx+data_len/2];
+        data_tmp[idx+data_len/2] = static_cast<int>(round(tmp*pow(2.0, 30.0)));
+    }
+    return;
+}
+
 void interleave(float **data_in, int num_after_alignment) {
   // num_after_alignment: number of bias after alignment
 
   float *ptr_uninterleaved = *data_in;
+  // fixed_scale_bias_new(ptr_uninterleaved, 2 * num_after_alignment);
   float *ptr_interleaved =
       (float *)fpga_malloc(2 * num_after_alignment * sizeof(float));  // NOLINT
   int num = num_after_alignment / 4;
diff --git a/mobile/src/fpga/V2/pe.cpp b/mobile/src/fpga/V2/pe.cpp
index cc9d8d20cd0d68bb16c851aea195b943a9dd18a9..aa150e0c6cecbdf278f3d776ebba4ec81ed003a1 100644
--- a/mobile/src/fpga/V2/pe.cpp
+++ b/mobile/src/fpga/V2/pe.cpp
@@ -79,7 +79,8 @@ using namespace std;     // NOLINT
 #define REG_CONVERT_CMD 0x400
 #define REG_CONVERT_SRC_ADDR 0x408
 #define REG_CONVERT_DST_ADDR 0x410
-#define REG_CONVERT_LENGTH 0x418
+#define REG_CONVERT_RD_LENGTH 0x418
+#define REG_CONVERT_WR_LENGTH 0x420
 
 /*resize*/
 #define REG_RESIZE_CMD 0x600
@@ -693,7 +694,8 @@ int PerformBypass(const struct BypassArgs &args) {
   reg_writeq(output_scale, REG_SCALE_PARAMETER);
   reg_writeq(input_address_phy, REG_CONVERT_SRC_ADDR);
   reg_writeq(output_address_phy, REG_CONVERT_DST_ADDR);
-  reg_writeq(datalen, REG_CONVERT_LENGTH);
+  reg_writeq(datalen, REG_CONVERT_RD_LENGTH);
+  reg_writeq(datalen, REG_CONVERT_WR_LENGTH);
   reg_writeq(cmd, REG_CONVERT_CMD);
   DLOG << "before reg poll";
   if (0 != fpga_regpoll(REG_INTERRUPT, INTERRUPT_BYPASS, PE_IRQ_TIMEOUT)) {
diff --git a/mobile/src/fpga/common/driver.cpp b/mobile/src/fpga/common/driver.cpp
index 71e3bf9746e600703fa43f2c8616f73806f6fd60..911704965aac3b6897b70dc60cb23fb4f3e59979 100644
--- a/mobile/src/fpga/common/driver.cpp
+++ b/mobile/src/fpga/common/driver.cpp
@@ -134,6 +134,7 @@ int fpga_regpoll(uint64_t reg, uint64_t val, int time) {
   uint64_t i = 0;
   /*timeout精确性待确认*/
   int64_t timeout = time * 6;
+  usleep(1);
 
   for (i = 0; i < timeout; i++) {
     if (val == reg_readq(reg)) {
diff --git a/mobile/src/operators/kernel/fpga/V2/anchor_generator_kernel.cpp b/mobile/src/operators/kernel/fpga/V2/anchor_generator_kernel.cpp
index 6046b3d2f0a4a1d273d31aac079244ce3ec3703a..951fbb5f3708bf511bfcbbb0669fb7a56a4eb7c4 100644
--- a/mobile/src/operators/kernel/fpga/V2/anchor_generator_kernel.cpp
+++ b/mobile/src/operators/kernel/fpga/V2/anchor_generator_kernel.cpp
@@ -45,9 +45,9 @@ bool AnchorGeneratorKernel<FPGA, float>::Init(
 
   if (offset > 0.6) {
     memcpy(anchors_offset, anchors_offset2, sizeof(anchors_offset));
-    std::cout << "anchor generator marker" << std::endl;
+    DLOG << "anchor generator marker";
   } else {
-    std::cout << "anchor generator rfcn" << std::endl;
+    DLOG << "anchor generator rfcn";
   }
   int num_anchors = sizeof(anchors_offset) / (sizeof(int) * 4);
 
diff --git a/mobile/src/operators/kernel/fpga/V2/proposal_kernel.cpp b/mobile/src/operators/kernel/fpga/V2/proposal_kernel.cpp
index ecc2577bd6ba9f8f21d4cccb94bdc27466b4a5d1..50179b9cd55d772f035e83fdc54ee98f38de1c54 100644
--- a/mobile/src/operators/kernel/fpga/V2/proposal_kernel.cpp
+++ b/mobile/src/operators/kernel/fpga/V2/proposal_kernel.cpp
@@ -30,16 +30,12 @@ bool ProposalKernel<FPGA, float>::Init(ProposalParam<FPGA> *param) {
   int64_t batch = param->scores_->dims()[0];
   auto total = post_nms_top_n * batch;
   param->rpn_rois_->mutable_data<float>({total, 4});
-  param->rpn_probs_->mutable_data<float>({total, 1});
+  param->rpn_probs_->mutable_data<int8_t>({total, 1});
 
   param->float_bbox = std::make_shared<Tensor>();
   param->float_bbox->Resize(param->bbox_deltas_->dims());
   param->float_bbox->init(type_id<float>().hash_code());
   fpga::format_fp32_ofm(param->float_bbox.get());
-  param->float_score = std::make_shared<Tensor>();
-  param->float_score->Resize(param->scores_->dims());
-  param->float_score->init(type_id<float>().hash_code());
-  fpga::format_fp32_ofm(param->float_score.get());
 
   auto input = param->scores_;
   param->score_index_ = std::make_shared<Tensor>();
@@ -88,7 +84,7 @@ void AppendProposals(Tensor *dst, int64_t offset, const Tensor &src) {
 
 template <class T>
 static inline void BoxCoder(Tensor *all_anchors, Tensor *bbox_deltas,
-                            Tensor *variances, Tensor *proposals) {
+                            Tensor *proposals) {
   T *proposals_data = proposals->mutable_data<T>();
 
   int64_t row = all_anchors->dims()[0];
@@ -96,10 +92,6 @@ static inline void BoxCoder(Tensor *all_anchors, Tensor *bbox_deltas,
 
   auto *bbox_deltas_data = bbox_deltas->data<T>();
   auto *anchor_data = all_anchors->data<T>();
-  const T *variances_data = nullptr;
-  if (variances) {
-    variances_data = variances->data<T>();
-  }
 
   for (int64_t i = 0; i < row; ++i) {
     T anchor_width = anchor_data[i * len + 2] - anchor_data[i * len] + 1.0;
@@ -244,10 +236,10 @@ static inline Tensor NMS(Tensor *bbox, Tensor *scores, T nms_threshold,
   // 4: [xmin ymin xmax ymax]
   int64_t box_size = bbox->dims()[1];
 
-  std::vector<T> scores_data(num_boxes);
-  std::copy_n(scores->data<T>(), num_boxes, scores_data.begin());
-  std::vector<std::pair<T, int>> sorted_indices =
-      GetSortedScoreIndex<T>(scores_data);
+  std::vector<int8_t> scores_data(num_boxes);
+  std::copy_n(scores->data<int8_t>(), num_boxes, scores_data.begin());
+  std::vector<std::pair<int8_t, int>> sorted_indices =
+      GetSortedScoreIndex<int8_t>(scores_data);
 
   std::vector<int> selected_indices;
   int selected_num = 0;
@@ -284,8 +276,7 @@ std::pair<Tensor, Tensor> ProposalForOneImage(
     const Tensor &scores_slice,       // [N, 1]
     const Tensor &score_index, int pre_nms_top_n, int post_nms_top_n,
     float nms_thresh, float min_size, float eta) {
-  auto *scores_data = scores_slice.data<T>();
-
+  auto *scores_data = scores_slice.data<int8_t>();
   // Sort index
   Tensor index_t;
   index_t.Resize({scores_slice.numel()});
@@ -306,17 +297,17 @@ std::pair<Tensor, Tensor> ProposalForOneImage(
   }
 
   Tensor scores_sel, bbox_sel, anchor_sel, var_sel;
-  scores_sel.mutable_data<T>({index_t.numel(), 1});
+  scores_sel.mutable_data<int8_t>({index_t.numel(), 1});
   bbox_sel.mutable_data<T>({index_t.numel(), 4});
   anchor_sel.mutable_data<T>({index_t.numel(), 4});
   var_sel.mutable_data<T>({index_t.numel(), 4});
 
-  CPUGather<T>(scores_slice, index_t, &scores_sel);
+  CPUGather<int8_t>(scores_slice, index_t, &scores_sel);
   CPUGather<T>(bbox_deltas_slice, index_t, &bbox_sel);
   CPUGather<T>(anchors, index_t, &anchor_sel);
   Tensor proposals;
   proposals.mutable_data<T>({index_t.numel(), 4});
-  BoxCoder<T>(&anchor_sel, &bbox_sel, nullptr, &proposals);
+  BoxCoder<T>(&anchor_sel, &bbox_sel, &proposals);
 
   ClipTiledBoxes<T>(im_info_slice, &proposals);
 
@@ -325,10 +316,10 @@ std::pair<Tensor, Tensor> ProposalForOneImage(
 
   Tensor scores_filter;
   bbox_sel.mutable_data<T>({keep.numel(), 4});
-  scores_filter.mutable_data<T>({keep.numel(), 1});
+  scores_filter.mutable_data<int8_t>({keep.numel(), 1});
 
   CPUGather<T>(proposals, keep, &bbox_sel);
-  CPUGather<T>(scores_sel, keep, &scores_filter);
+  CPUGather<int8_t>(scores_sel, keep, &scores_filter);
   if (nms_thresh <= 0) {
     return std::make_pair(bbox_sel, scores_filter);
   }
@@ -341,10 +332,10 @@ std::pair<Tensor, Tensor> ProposalForOneImage(
   }
 
   proposals.mutable_data<T>({keep_nms.numel(), 4});   // original
-  scores_sel.mutable_data<T>({keep_nms.numel(), 1});  // original
+  scores_sel.mutable_data<int8_t>({keep_nms.numel(), 1});  // original
 
   CPUGather<T>(bbox_sel, keep_nms, &proposals);
-  CPUGather<T>(scores_filter, keep_nms, &scores_sel);
+  CPUGather<int8_t>(scores_filter, keep_nms, &scores_sel);
   return std::make_pair(proposals, scores_sel);
 }
 
@@ -368,69 +359,43 @@ void ProposalKernel<FPGA, float>::Compute(const ProposalParam<FPGA> &param) {
   bbox_height = (uint32_t)(input_bbox->dims()[2]);
   bbox_width = (uint32_t)(input_bbox->dims()[3]);
 
-  std::shared_ptr<Tensor> score_tmp = std::make_shared<Tensor>();
-  score_tmp->Resize(param.scores_->dims());
-  score_tmp->mutable_data<int8_t>();
-
-  std::shared_ptr<Tensor> bbox_tmp = std::make_shared<Tensor>();
-  bbox_tmp->Resize(param.bbox_deltas_->dims());
-  bbox_tmp->mutable_data<int8_t>();
-
-  auto score_tmp_data = score_tmp->data<int8_t>();
-  auto bbox_tmp_data = bbox_tmp->data<int8_t>();
   int64_t amount_per_side = score_width * score_height;
-  int idx = 0;
+
   int alignedCW =
       fpga::align_to_x(score_width * score_channels, IMAGE_ALIGNMENT);
   int unalignedCW = score_width * score_channels;
   fpga::fpga_invalidate(input_score_data,
                         score_height * alignedCW * sizeof(int8_t));
+
+  Tensor score_tensor = *input_score;
   for (int h = 0; h < score_height; h++) {
     for (int w = 0; w < score_width; w++) {
-      for (int c = 0; c < score_channels; c++) {
-        if (alignedCW == unalignedCW) {
-          *(score_tmp_data + c * amount_per_side + score_width * h + w) =
-              (*(input_score_data++));
-        } else {
-          idx = h * alignedCW + w * score_channels + c;
-          *(score_tmp_data + c * amount_per_side + score_width * h + w) =
-              input_score_data[idx];
-        }
+      for (int c = 0; c < score_channels; ++c) {
+        int dstidx = h*unalignedCW + w*score_channels + c;
+        int srcidx = h*alignedCW + w*score_channels + c;
+        score_tensor.data<int8_t>()[dstidx] = input_score_data[srcidx];
       }
     }
   }
+
   amount_per_side = bbox_width * bbox_height;
   alignedCW = fpga::align_to_x(bbox_width * bbox_channels, IMAGE_ALIGNMENT);
   unalignedCW = bbox_width * bbox_channels;
   fpga::fpga_invalidate(input_bbox_data,
                         bbox_height * alignedCW * sizeof(int8_t));
+
+  auto bbox_tensor = param.float_bbox.get();
   for (int h = 0; h < bbox_height; h++) {
     for (int w = 0; w < bbox_width; w++) {
-      for (int c = 0; c < bbox_channels; c++) {
-        if (alignedCW == unalignedCW) {
-          *(bbox_tmp_data + c * amount_per_side + bbox_width * h + w) =
-              (*(input_bbox_data++));
-        } else {
-          idx = h * alignedCW + w * bbox_channels + c;
-          *(bbox_tmp_data + c * amount_per_side + bbox_width * h + w) =
-              input_bbox_data[idx];
-        }
+      for (int c = 0; c < bbox_channels; ++c) {
+        int dstidx = h*unalignedCW + w*bbox_channels + c;
+        int srcidx = h*alignedCW + w*bbox_channels + c;
+        bbox_tensor->data<float>()[dstidx] =
+            (static_cast<int>(input_bbox_data[srcidx]))/127.0*
+               input_bbox->scale[0];
       }
     }
   }
-
-  auto score_tensor = param.float_score.get();
-  for (int i = 0; i < score_height * score_width * score_channels; i++) {
-    score_tensor->data<float>()[i] =
-        score_tmp_data[i] / 127.0 * input_score->scale[0];
-  }
-  auto bbox_tensor = param.float_bbox.get();
-  for (int i = 0; i < bbox_height * bbox_width * bbox_channels; i++) {
-    bbox_tensor->data<float>()[i] =
-        bbox_tmp_data[i] / 127.0 * input_bbox->scale[0];
-  }
-  auto *scores = param.float_score.get();
-  auto *bbox_deltas = param.float_bbox.get();
   auto *im_info = param.im_info_;
   auto anchors = *param.anchors_;
   auto variances = *param.variances_;
@@ -447,37 +412,23 @@ void ProposalKernel<FPGA, float>::Compute(const ProposalParam<FPGA> &param) {
   float min_size = param.min_size_;
   float eta = param.eta_;
 
-  auto &scores_dim = scores->dims();
-  int64_t num = scores_dim[0];
-  int64_t c_score = scores_dim[1];
-  int64_t h_score = scores_dim[2];
-  int64_t w_score = scores_dim[3];
-
-  auto &bbox_dim = bbox_deltas->dims();
-  int64_t c_bbox = bbox_dim[1];
-  int64_t h_bbox = bbox_dim[2];
-  int64_t w_bbox = bbox_dim[3];
-
-  //
-  rpn_rois->mutable_data<float>({bbox_deltas->numel(), 4});
-  rpn_roi_probs->mutable_data<float>({scores->numel(), 1});
-
+  rpn_rois->mutable_data<float>({bbox_tensor->numel()/4, 4});
+  rpn_roi_probs->mutable_data<int8_t>({input_score->numel()/4, 1});
   framework::LoD lod;
   lod.resize(1);
   auto &lod0 = lod[0];
   lod0.push_back(0);
-  anchors.Resize({anchors.numel(), 4});
-  variances.Resize({variances.numel(), 4});
+  anchors.Resize({anchors.numel()/4, 4});
+  variances.Resize({variances.numel()/4, 4});
 
   int64_t num_proposals = 0;
-  for (int64_t i = 0; i < num; ++i) {
+  for (int64_t i = 0; i < score_n; ++i) {
     Tensor im_info_slice = im_info->Slice(i, i + 1);
     Tensor bbox_deltas_slice = (*bbox_tensor).Slice(i, i + 1);
-    Tensor scores_slice = (*score_tensor).Slice(i, i + 1);
-
-    bbox_deltas_slice.Resize({h_bbox * w_bbox * c_bbox, 4});
-    scores_slice.Resize({h_score * w_score * c_score, 1});
+    Tensor scores_slice = score_tensor.Slice(i, i + 1);
 
+    bbox_deltas_slice.Resize({bbox_height * bbox_width * bbox_channels / 4, 4});
+    scores_slice.Resize({score_height * score_width * score_channels, 1});
     std::pair<Tensor, Tensor> tensor_pair = ProposalForOneImage<float>(
         im_info_slice, anchors, variances, bbox_deltas_slice, scores_slice,
         score_index, pre_nms_top_n, post_nms_top_n, nms_thresh, min_size, eta);
diff --git a/mobile/src/operators/kernel/fpga/V2/psroi_pool_kernel.cpp b/mobile/src/operators/kernel/fpga/V2/psroi_pool_kernel.cpp
index b8b5202e27369a74430aa130db68501ff6891eec..87948f824e353ef3a3c341a0a1ecee5957e871d6 100644
--- a/mobile/src/operators/kernel/fpga/V2/psroi_pool_kernel.cpp
+++ b/mobile/src/operators/kernel/fpga/V2/psroi_pool_kernel.cpp
@@ -44,14 +44,14 @@ bool PSRoiPoolKernel<FPGA, float>::Init(PSRoiPoolParam<FPGA>* param) {
 }
 
 template <typename Dtype>
-void PSROIPoolingForward(const Dtype* bottom_data, const int height,
+void PSROIPoolingForward(const int8_t* bottom_data, const int height,
                          const int width, const int input_channel,
                          Dtype* top_data, const int pooled_height,
                          const int pooled_width, const int output_channel,
                          const Dtype* bottom_rois, const Dtype Bin_size_h,
                          const Dtype Bin_size_w, const Dtype roi_start_h,
                          const Dtype roi_start_w, const int pw, const int ph,
-                         const int roi_batch_ind) {
+                         float scale, const int roi_batch_ind) {
   int hstart = floor(static_cast<Dtype>(ph) * Bin_size_h + roi_start_h);
   int wstart = floor(static_cast<Dtype>(pw) * Bin_size_w + roi_start_w);
   int hend = ceil(static_cast<Dtype>(ph + 1) * Bin_size_h + roi_start_h);
@@ -64,11 +64,12 @@ void PSROIPoolingForward(const Dtype* bottom_data, const int height,
   wend = std::min(std::max(wend, 0), width);
   bool is_empty = (hend <= hstart) || (wend <= wstart);
 
-  float sum_pixels_c[output_channel] = {0};
-  float pixels_c[output_channel] = {0};
+  float avg_pixels_c[output_channel] = {0};
+  int sum_pixels_c[output_channel] = {0};
+  int8_t pixels_c[output_channel] = {0};
   if (!is_empty) {
     Dtype bin_area = (hend - hstart) * (wend - wstart);
-    float rec_bin_area = 1 / bin_area;
+    float scale_fuse = scale / bin_area;
 
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
@@ -86,27 +87,21 @@ void PSROIPoolingForward(const Dtype* bottom_data, const int height,
       }
     }
     for (int output_c = 0; output_c < output_channel; output_c++) {
-      sum_pixels_c[output_c] *= rec_bin_area;
+      avg_pixels_c[output_c] = sum_pixels_c[output_c] * scale_fuse;
     }
   }
 
   int output_index_base = (ph * pooled_width + pw) * output_channel;
   top_data += output_index_base;
-  memcpy(top_data, sum_pixels_c, output_channel * 4);
+  memcpy(top_data, avg_pixels_c, output_channel * 4);
 }
 
 template <>
 void PSRoiPoolKernel<FPGA, float>::Compute(const PSRoiPoolParam<FPGA>& param) {
   auto input_tensor = param.input_x_;
   auto input_data = input_tensor->data<int8_t>();
-  auto Si = input_tensor->scale[0];
-  auto float_input_tensor = param.float_input.get();
-  auto float_input_data = float_input_tensor->data<float>();
-  for (int i = 0; i < float_input_tensor->numel(); i++) {
-    float_input_data[i] = input_data[i] / 127.0 * Si;
-  }
-
-  auto* in = float_input_tensor;
+  auto scale = input_tensor->scale[0] / 127.0;
+  fpga::fpga_invalidate(input_data, input_tensor->numel() * sizeof(int8_t));
   auto* rois = param.input_rois_;
   auto* out = param.output_;
 
@@ -115,22 +110,19 @@ void PSRoiPoolKernel<FPGA, float>::Compute(const PSRoiPoolParam<FPGA>& param) {
   auto spatial_scale = param.spatial_scale_;
   auto output_channels = param.output_channels_;
 
-  auto in_dims = in->dims();
+  auto in_dims = input_tensor->dims();
   int batch_size = in_dims[0];
   int input_channels = in_dims[1];
   int height = in_dims[2];
   int width = in_dims[3];
   int rois_num = rois->dims()[0];
 
-  auto data_nhwc = in->mutable_data<float>();
-
   framework::DDim dims_out_new = framework::make_ddim(
       {rois_num, (param.output_)->dims()[1], (((param.output_)->dims()[2])),
        (param.output_)->dims()[3]});
 
   (param.output_)->Resize(dims_out_new);
 
-  const float* input_data_tmp = data_nhwc;  // in->data<float>();
   framework::Tensor rois_batch_id_list;
   rois_batch_id_list.Resize({rois_num});
   auto rois_batch_id_data = rois_batch_id_list.mutable_data<int>();
@@ -151,12 +143,7 @@ void PSRoiPoolKernel<FPGA, float>::Compute(const PSRoiPoolParam<FPGA>& param) {
       "the channels of input X should equal the product of "
       "output_channels x pooled_height x pooled_width");
 
-  // calculate batch id index for each roi according to LoD
-  for (int n = 0; n < rois_batch_size; ++n) {
-    for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
-      rois_batch_id_data[i] = n;
-    }
-  }
+
   auto output_data = out->mutable_data<float>();
   auto input_rois = rois->data<float>();
 
@@ -187,10 +174,10 @@ void PSRoiPoolKernel<FPGA, float>::Compute(const PSRoiPoolParam<FPGA>& param) {
     for (int ph = 0; ph < pooled_height; ph++) {
       for (int pw = 0; pw < pooled_width; pw++) {
         PSROIPoolingForward<float>(
-            input_data_tmp, height, width, input_channels, offset_output_data,
+            input_data, height, width, input_channels, offset_output_data,
             pooled_height, pooled_width, output_channels, input_rois,
             bin_size_h, bin_size_w, roi_start_h, roi_start_w, pw, ph,
-            roi_batch_ind);
+            scale, roi_batch_ind);
       }
     }
   }
diff --git a/mobile/src/operators/kernel/fpga/V2/reshape2_kernel.cpp b/mobile/src/operators/kernel/fpga/V2/reshape2_kernel.cpp
index ebaf3759400c60c9ecf36467d0eeb7adad140f46..fcf0889b4a66919efc677e211a1da453fd761de4 100644
--- a/mobile/src/operators/kernel/fpga/V2/reshape2_kernel.cpp
+++ b/mobile/src/operators/kernel/fpga/V2/reshape2_kernel.cpp
@@ -25,6 +25,7 @@ bool Reshape2Kernel<FPGA, float>::Init(Reshape2Param<FPGA> *param) {
   auto input = const_cast<LoDTensor *>(param->InputX());
   auto output = param->Out();
   auto shape = param->Shape();
+  output->scale[0] = input->scale[0];
 
   auto num_in = framework::product(input->dims());
   auto num_shape = framework::product(framework::make_ddim(shape));
diff --git a/mobile/src/operators/kernel/fpga/V2/softmax_kernel.cpp b/mobile/src/operators/kernel/fpga/V2/softmax_kernel.cpp
index b7615a8891b8292dd4d65c15955a0ee640c2f770..843f249c683717789999db733a04b3da0198bdcb 100755
--- a/mobile/src/operators/kernel/fpga/V2/softmax_kernel.cpp
+++ b/mobile/src/operators/kernel/fpga/V2/softmax_kernel.cpp
@@ -81,6 +81,7 @@ void SoftmaxKernel<FPGA, float>::Compute(const SoftmaxParam<FPGA> &param) {
   auto w = 1;
   auto c = 1;
   if (dims.size() == 4) {
+    n = dims[0];
     h = dims[1];
     w = dims[2];
     c = dims[3];
@@ -90,6 +91,7 @@ void SoftmaxKernel<FPGA, float>::Compute(const SoftmaxParam<FPGA> &param) {
       h = 1;
     }
   } else if (dims.size() == 2) {
+    n = dims[0];
     c = dims[1];
   }
   if ((c == 2) && (in_x->type() == type_id<int8_t>())) {