[Add] benchmark mechanism

What commit version of aidge do you use

• aidge bundle: 0.5.1_dev

Issue Summary

Aidge currently lacks a standardized and easy-to-use benchmarking mechanism for evaluating the performance and correctness of different modules. To ensure efficient model execution and accurate inference across various backends, we need a flexible benchmarking script.

Motivation

Aidge provides multiple module options for running deep learning models. A proper benchmarking framework will:

• Help compare inference time across different execution backends.
• Verify that inference outputs are consistent across different configurations.
• Assist in optimizing implementations by identifying performance bottlenecks.

Expected Features

The benchmarking script should:

• Measure Inference Time
• Compare inference results between the assessed modules and ONNXRuntime.
• Support testing
  • individual ONNX operator
    • custom input size
    • custom attributes
  • Aidge operators
    • custom input size
    • custom attributes
  • multiple-nodes ONNX model
• Include configurable options for
  • number of warmup and test runs
  • set of tests or individual model
• Support JSON output for use of benchmark results in other scripts.
• Allow users to specify the library to assess (Aidge module, PyTorch, ONNXRuntime).
• Be usable for
  • Aidge unit-tests
  • as independant script