[Feature]: Add execution provider selection and inference benchmarking

🚀 Feature Description

Add execution provider selection (CPU/CUDA) and multi-iteration inference benchmarking to the existing examples/onnx example.

📂 Feature Category

Testing Infrastructure

💡 Motivation

The current examples/onnx example only runs inference using the default CPU execution provider with a single pass. This doesn't reflect real-world applications where backend selection and accurate latency measurement are critical. When evaluating models for deployment on different hardware (CPU vs GPU), having a quick way to compare execution providers within the same example is essential.

💭 Proposed Solution

• Add a --device CLI flag to select the execution provider (cpu or cuda)
• Add a --num-iterations CLI flag for multi-run benchmarking (default: 10)
• Add a warm-up run before timed iterations to exclude cold-start overhead (memory allocation, graph optimization, kernel compilation)
• Print latency statistics (mean, min, max) across iterations
• Update README with usage examples for both CPU and CUDA execution

📚 Library Reference

• ONNX Runtime Execution Providers: https://onnxruntime.ai/docs/execution-providers/
• ort crate CUDA EP: https://docs.rs/ort/latest/ort/execution_providers/cuda/
• Current examples/onnx implementation in this repo

🔄 Alternatives Considered

Considered creating a completely new example, but improving the existing one keeps codebase lean and provides immediate value to current users of the ONNX example.

🎯 Use Cases

• Comparing inference latency across CPU and CUDA backends for model deployment decisions
• Providing a starting point for users who want to run ONNX models with GPU acceleration
• Laying groundwork for TensorRT execution provider support (related: #634, GSoC 2026 VLM Inference project)

📝 Additional Context

This is a small, focused improvement to an existing example. The changes are compatible, default behavior remains CPU with a single run if no extra flags are provided.

🤝 Contribution Intent

• I plan to submit a PR to implement this feature
• I'm requesting this feature but not planning to implement it