标题： 面向资源受限设备的高效皮肤疾病分类的量化感知神经形态架构 显示英文标题

标题： Quantization-Aware Neuromorphic Architecture for Efficient Skin Disease Classification on Resource-Constrained Devices

摘要： 在边缘设备上进行准确且高效的皮肤病变分类对于可及的皮肤病学护理至关重要，但由于计算、能源和隐私限制，这仍然具有挑战性。 我们引入了QANA，这是一种新颖的量化感知神经形态架构，用于在资源受限的硬件上进行增量皮肤病变分类。 QANA有效地集成了幽灵模块、高效通道注意力和挤压-激励块，以实现低延迟和节能的特征表示。 其量化感知头部和脉冲兼容转换使得无缝转换到脉冲神经网络（SNNs）并在神经形态平台上部署成为可能。 在大规模HAM10000基准和一个真实世界临床数据集上的评估表明，QANA在HAM10000上实现了91.6%的Top-1准确率和82.4%的宏F1，在临床数据集上实现了90.8%/81.7%，在公平比较中显著优于最先进的CNN-to-SNN模型。 部署在BrainChip Akida硬件上，QANA实现了1.5毫秒的推理延迟和每张图像1.7毫焦耳的能量消耗，与基于GPU的CNN相比，推理延迟和能耗分别减少了超过94.6%/98.6%，超过了最先进的CNN-to-SNN转换基线。 这些结果证明了QANA在边缘环境中进行准确、实时和隐私敏感的医疗分析的有效性。 显示英文摘要

摘要： Accurate and efficient skin lesion classification on edge devices is critical for accessible dermatological care but remains challenging due to computational, energy, and privacy constraints. We introduce QANA, a novel quantization-aware neuromorphic architecture for incremental skin lesion classification on resource-limited hardware. QANA effectively integrates ghost modules, efficient channel attention, and squeeze-and-excitation blocks for robust feature representation with low-latency and energy-efficient inference. Its quantization-aware head and spike-compatible transformations enable seamless conversion to spiking neural networks (SNNs) and deployment on neuromorphic platforms. Evaluation on the large-scale HAM10000 benchmark and a real-world clinical dataset shows that QANA achieves 91.6\% Top-1 accuracy and 82.4\% macro F1 on HAM10000, and 90.8\% / 81.7\% on the clinical dataset, significantly outperforming state-of-the-art CNN-to-SNN models under fair comparison. Deployed on BrainChip Akida hardware, QANA achieves 1.5\,ms inference latency and 1.7\,mJ energy per image, reducing inference latency and energy use by over 94.6\%/98.6\% compared to GPU-based CNNs surpassing state-of-the-art CNN-to-SNN conversion baselines. These results demonstrate the effectiveness of QANA for accurate, real-time, and privacy-sensitive medical analysis in edge environments.