Adaptive Neural Network Quantization for AI Chip Memory Efficiency

The patent applies parameter changes by dynamically adjusting the precision (bit width) of data representations during different phases of neural network operation. During training, higher precision (e.g., float32) is used to maintain accuracy, while during inference, lower precision (e.g., quantized 8-bit integers) is used to reduce storage and computational overhead. This dynamic parameter adjustment resolves the contradiction between maintaining measurement precision and reducing data volume.

The patent implements parameter changes by switching between different data precision levels based on the operational phase. During training, full precision is maintained for accurate gradient computation, but during inference, the system transitions to lower precision representations that require less processing bandwidth while maintaining acceptable accuracy. This resolves the contradiction between measurement precision and processing bandwidth requirements.

The patent applies dynamics by making the precision level adaptive rather than static. The system dynamically selects the appropriate precision level based on the operational context: using high precision during training when accuracy is critical, and switching to low precision during inference when storage efficiency is prioritized. This dynamic adaptation resolves the contradiction between reducing data volume and maintaining measurement precision.

The patent applies preliminary action by performing quantization parameter calibration and model adaptation during the training phase before deployment. The system pre-computes quantization parameters, scales, and offsets, and stores them alongside the model weights. This preliminary preparation eliminates the need for complex runtime quantization calculations, thereby reducing storage space while avoiding significant increases in device complexity during actual inference operations.