Operation Conversion Toolchains

The conversion toolchain is employed to transform a trained non-optimized AI model through a sequence of micro conversion steps, culminating in the creation of a highly optimized model that leverages the target hardware architecture to its fullest potential. Note that a conversion toolchain has the capability to generate multiple optimized model files, each tailored to a specific version of the hardware. Additionally, it may decline to optimize a model if the target hardware does not support a particular model.

Those conversion steps may include:

• Model Pruning: Pruning involves removing unnecessary connections or parameters from the model without significantly impacting its performance. This reduces the model's size and computational requirements, making it more efficient during inference.

• Quantization: Quantization involves reducing the precision of the model's weights and activations, typically from floating-point numbers to lower precision fixed-point numbers (e.g., 8-bit integers). This reduces memory usage and computational complexity, leading to faster inference.

• Model Compression: Techniques such as knowledge distillation or model distillation involve training a smaller model to mimic the behavior of a larger, more complex model. This smaller model can then be used for inference, offering a trade-off between model size and performance.

• Hardware-specific Optimization: Tailor the model to leverage specific hardware accelerators like CPUs, GPUs, NPUs, or specialized inference chips.

For example, Nvidia utilizes TensorRT™ to optimize an ONNX model, generating a serialized TensorRT engines from models that is subsequently employed by the runtime to execute inference tasks efficiently. Similarly, Hailo provides an SDK called the Dataflow Compiler to compile ONNX models to a HEF files used by their HailoRT runtime.