diff --git a/onnxruntime/core/providers/cpu/math/element_wise_ops.cc b/onnxruntime/core/providers/cpu/math/element_wise_ops.cc
index bfc134b0f0..8f48195ea0 100644
--- a/onnxruntime/core/providers/cpu/math/element_wise_ops.cc
+++ b/onnxruntime/core/providers/cpu/math/element_wise_ops.cc
@@ -389,10 +389,23 @@ Status Sqrt<float>::Compute(OpKernelContext* ctx) const {
 
 template <>
 Status Pow<float>::Compute(OpKernelContext* context) const {
+  const Tensor& Y = *context->Input<Tensor>(1);
+  std::function<void(EigenVectorMap<float>, ConstEigenVectorMap<float>, float)> input1scalar =
+      [](EigenVectorMap<float> output, ConstEigenVectorMap<float> input0, float input1) { output = Eigen::pow(input0.array(), input1); };
+  if (Y.Shape().Size() == 1) {
+    float value = * Y.Data<float>();
+    if (value == 2.0) {
+      input1scalar = [](EigenVectorMap<float> output, ConstEigenVectorMap<float> input0, float) { output = Eigen::square(input0.array()); };
+    }
+    else if (value == 3.0) {
+      input1scalar = [](EigenVectorMap<float> output, ConstEigenVectorMap<float> input0, float) { output = Eigen::cube(input0.array()); };
+    }
+  }
+
   return BroadcastTwo<float, float>(
       *context,
       [](EigenVectorMap<float> output, float input0, ConstEigenVectorMap<float> input1) { output = Eigen::pow(input0, input1.array()); },
-      [](EigenVectorMap<float> output, ConstEigenVectorMap<float> input0, float input1) { output = Eigen::pow(input0.array(), input1); },
+      input1scalar,
       [](EigenVectorMap<float> output, ConstEigenVectorMap<float> input0, ConstEigenVectorMap<float> input1) { output = Eigen::pow(input0.array(), input1.array()); });
 }