<![CDATA[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.]]>
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