Eye-based emotion recognition enables eyewear devices to perceive users' emotional states and support emotion-aware interaction. However, deploying such functionality on their resource-limited embedded hardware remains challenging. Time-to-first-spike (TTFS)-coded spiking neural networks (SNNs) offer a promising solution due to their extremely sparse and energy-efficient computation, where each neuron emits at most one binary spike. While prior works have primarily focused on improving TTFS SNN training algorithms, the role of network architecture has been largely overlooked. This is particularly critical, as spike timing in TTFS SNNs is tightly coupled with architectural design, and eye-based emotion recognition requires compact yet highly efficient networks. In this paper, we propose TNAS-ER, the first neural architecture search (NAS) framework tailored to TTFS SNNs for eye-based emotion recognition. TNAS-ER presents a novel ANN-assisted search strategy that leverages a ReLU-based ANN counterpart to guide architecture optimization and stabilize training of the TTFS SNN. TNAS-ER employs an evolutionary algorithm, with weighted and unweighted average recall jointly defined as fitness objectives for emotion recognition. Extensive experiments demonstrate that TNAS-ER achieves high recognition performance with significantly improved efficiency. Furthermore, we evaluate TNAS-ER on a neuromorphic hardware, confirming its superior energy efficiency and strong potential for real-world applications.

翻译：基于眼部的情感识别使得可穿戴眼镜设备能够感知用户的情绪状态，并支持情感感知交互。然而，在资源受限的嵌入式硬件上部署此类功能仍具挑战性。首脉冲时间编码的脉冲神经网络（TTFS SNN）因具有极端稀疏性和高能效计算特性（每个神经元最多发射一个二进制脉冲）而成为极具前景的解决方案。虽然现有研究主要聚焦于改进TTFS SNN的训练算法，但网络架构的作用在很大程度上被忽视了。这一问题尤为关键，因为TTFS SNN中的脉冲时序与架构设计紧密耦合，而基于眼部的情感识别需要紧凑且高效的网络。本文提出TNAS-ER——首个专为眼部情感识别任务定制TTFS SNN的神经架构搜索（NAS）框架。TNAS-ER提出一种创新的ANN辅助搜索策略，通过基于ReLU的ANN对等网络引导架构优化并稳定TTFS SNN训练。该框架采用进化算法，将加权平均召回率与未加权平均召回率联合定义为情感识别的适应度目标。大量实验表明，TNAS-ER在显著提升效率的同时实现了高识别性能。此外，我们在神经形态硬件上对TNAS-ER进行了评估，验证了其卓越的能效优势及在真实场景中的巨大应用潜力。