Calibrator for AI-on-Chips is designed to enhance precision and performance in AI System-on-Chips using post-training quantization techniques. By employing architecture-aware algorithms, this calibrator maintains high accuracy levels even in fixed-point architectures such as INT8. It supports heterogeneous multicore devices, ensuring compatibility with various processing engines and bit-width configurations.
The product utilizes a sophisticated precision simulator for accurate quantization across data paths, leveraging hardware-specific controls for precise calibration. The included calibration workflow efficiently produces a quantization table that seamlessly integrates with compilers to fine-tune model precision without altering neural network topologies.
Supporting interoperability with popular frameworks, the Calibrator for AI-on-Chips enhances performance without necessitating retraining. Users benefit from expedited quantization processes, which reduce the precision drop to minimal levels, thus ensuring high-quality outputs even for complex AI models.
