The ONNC Calibrator is engineered to ensure high precision in AI System-on-Chips using post-training quantization (PTQ) techniques. This tool enables architecture-aware quantization, which helps maintain 99.99% precision even with fixed-point architecture, such as INT8. Designed for diverse heterogeneous multicore setups, it supports multiple engines within a single chip architecture and employs rich entropy calculation techniques.
A major advantage of the ONNC Calibrator is its efficiency; it significantly reduces the time required for quantization, taking only seconds to process standard computer vision models. Unlike re-training methods, PTQ is non-intrusive, maintains network topology, and adapts based on input distribution to provide quick and precise quantization suitable for modern neural network frameworks such as ONNX and TensorFlow.
Furthermore, the Calibrator's internal precision simulator uses hardware control registers to maintain precision, demonstrating less than 1% precision drop in most computer vision models. It adapts flexibly to various hardware through its architecture-aware algorithms, making it a powerful tool for maintaining the high performance of AI systems.
