diff --git a/tensorflow/core/kernels/dequantize_op.cc b/tensorflow/core/kernels/dequantize_op.cc
index 28940e08494..2bf7b9c1834 100644
--- a/tensorflow/core/kernels/dequantize_op.cc
+++ b/tensorflow/core/kernels/dequantize_op.cc
@@ -41,11 +41,6 @@ template <typename Device, typename T>
 class DequantizeOp : public OpKernel {
  public:
   explicit DequantizeOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
-    half_range_ = !std::is_signed<T>::value
-                      ? 0.0f
-                      : (static_cast<float>(std::numeric_limits<T>::max()) -
-                         std::numeric_limits<T>::min() + 1) /
-                            2.0f;
     string mode_string;
     OP_REQUIRES_OK(ctx, ctx->GetAttr("mode", &mode_string));
     OP_REQUIRES(ctx,
@@ -67,6 +62,12 @@ class DequantizeOp : public OpKernel {
     const Tensor& input = ctx->input(0);
     const float min_range = ctx->input(1).flat<float>()(0);
     const float max_range = ctx->input(2).flat<float>()(0);
+    const float half_range =
+        !std::is_signed<T>::value
+            ? 0.0f
+            : (static_cast<float>(std::numeric_limits<T>::max()) -
+               std::numeric_limits<T>::min() + 1) /
+                  2.0f;
 
     Tensor* output = nullptr;
     OP_REQUIRES_OK(ctx, ctx->allocate_output(0, input.shape(), &output));
@@ -76,15 +77,11 @@ class DequantizeOp : public OpKernel {
           (static_cast<float>(std::numeric_limits<T>::max()) -
            std::numeric_limits<T>::min());
 
-      float* out_ptr = output->flat<float>().data();
-      const T* in_ptr = input.flat<T>().data();
+      const auto& input_tensor = input.flat<T>();
+      output->flat<float>().device(ctx->eigen_device<Device>()) =
+          ((input_tensor.template cast<float>() + half_range) * scale_factor) +
+          min_range;
 
-      const int64 num_elements = input.NumElements();
-      for (int i = 0; i < num_elements; ++i) {
-        out_ptr[i] =
-            ((static_cast<int>(in_ptr[i]) + half_range_) * scale_factor) +
-            min_range;
-      }
     } else if (mode_ == QUANTIZE_MODE_MIN_FIRST) {
       if (meta::IsSupportedAndEnabled() && std::is_same<T, quint8>()) {
         auto input_ui8_array = input.flat<quint8>();
@@ -101,17 +98,14 @@ class DequantizeOp : public OpKernel {
               ? (max_range / std::numeric_limits<T>::max())
               : std::max(min_range / std::numeric_limits<T>::min(),
                          max_range / std::numeric_limits<T>::max());
-      float* out_ptr = output->flat<float>().data();
-      const T* in_ptr = input.flat<T>().data();
-      const int64 num_elements = input.NumElements();
-      for (int64 i = 0; i < num_elements; ++i) {
-        out_ptr[i] = static_cast<int>(in_ptr[i]) * scale_factor;
-      }
+      const auto& input_tensor = input.flat<T>();
+      output->flat<float>().device(ctx->eigen_device<Device>()) =
+          input_tensor.template cast<int>().template cast<float>() *
+          scale_factor;
     }
   }
 
  private:
-  float half_range_;
   int mode_;
 };