Deep learning applications increasingly demand specialized hardware for efficient inference and, in some cases, on-device training. Neural Processing Units (NPUs) have emerged as dedicated accelerators designed to meet these computational demands, offering significant advantages over traditional CPUs and even general-purpose GPUs for specific deep learning workloads. Understanding their architecture and optimization strategies is paramount for developers aiming to deploy high-performance, power-efficient AI solutions, particularly
