Performance Analysis of Deep Learning Model Quantization on NPU for Real-Time Automatic License Plate Recognition Implementation

Neural Processing Units (NPUs) are dedicated accelerators designed to perform efficient deep learning inference on edge devices with limited computational and power resources. In real-time applications such as automated parking systems, accurate and low-latency license plate recognition is critical. This study evaluates the effectiveness of quantization techniques, specifically Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT), in improving the performance of YOLOv8-based license plate detection models deployed on an Intel NPU integrated within the Core Ultra 7 155H processor. Three model configurations are compared: a full-precision float32 model, a PTQ model, and a QAT model. All models are converted to OpenVINO’s Intermediate Representation (IR) and benchmarked using the benchmark_app tool. Results show that PTQ and QAT significantly enhance inference efficiency. QAT achieves up to 39.9% improvement in throughput and 28.6% reduction in latency compared to the non-quantized model, while maintaining higher detection accuracy. Both quantized models also reduce model size by nearly 50 percent. Although PTQ is simpler to implement, QAT offers a better balance between accuracy and speed, making it more suitable for deployment in edge scenarios with real-time constraints. These findings highlight QAT as an optimal strategy for efficient and accurate license plate recognition on NPU-based edge platforms.