[Performance] running on xavier gpu but cpu usage high

### Describe the issue

running on xavier gpu using cuda provider but cpu usage high.
By using command top, I can see it used gpu successfully(8~9 fps). But it also used 150% of one core cpu. I want to know why and how to solve. If i use cpulimit to control it under 50%, the inference time become slower(3 fps).
Can iobinding help? Was it because of some of the ops were not supported on cuda and it has to calculate and copy data on cpu?
The onnx model can be generated and inferenced successfully with opset16 without other custom plugin or something.
I searched issues and found that most related issues care about memory, not cpu usage.

### To reproduce

my code is like: (could also use some code advices)
this->env = Ort::Env(OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING, "lane");
Ort::SessionOptions session_options;
OrtSessionOptionsAppendExecutionProvider_CUDA(session_options, 0);
session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
Ort::AllocatorWithDefaultOptions allocator;
this->session = new Ort::Session(this->env, model_path.c_str(), session_options);
auto input_tensors = Ort::Value::CreateTensor(memory_info, (float*)Transed_t.data, this->width * this->height * 3,
this->input_rgb_dims.data(), this->input_rgb_dims.size());
std::vector output_tensors;
output_tensors = this->session->Run(Ort::RunOptions{nullptr},
input_node_names.data(),
&input_tensors, // input tensors
1, // 1
output_node_names.data(),
output_node_names.size()); // 5

### Urgency

very urgency

### Platform

Linux

### OS Version

4.9.201

### ONNX Runtime Installation

Built from Source

### ONNX Runtime Version or Commit ID

1.12

### ONNX Runtime API

C++

### Architecture

ARM64

### Execution Provider

CUDA

### Execution Provider Library Version

cuda 10.2 (jetpack 4.5)

### Model File

_No response_

### Is this a quantized model?

No

The issue provides no repository file, model, or test to start from. Reproduce the supplied C++ Ort::Session and Run setup on the stated Xavier ARM64/Linux environment, then investigate execution-provider behavior and operator support. Done means identifying the cause of the CPU usage and documenting or verifying a resolution.

Written by the indexing model from the issue text.