Deploying trustworthy artificial intelligence on edge robotics imposes a difficult trade-off between high-assurance robustness and energy sustainability. Traditional defense mechanisms against adversarial attacks typically incur significant computational overhead, threatening the viability of power-constrained platforms in environments such as cislunar space. This paper quantifies the energy cost of assurance in event-driven neuromorphic systems. We benchmark the Hierarchical Temporal Defense (HTD) framework on the BrainChip Akida AKD1000 processor against a suite of adversarial temporal attacks. We demonstrate that unlike traditional deep learning defenses which often degrade efficiency significantly with increased robustness, the event-driven nature of the proposed architecture achieves a superior trade-off. The system reduces gradient-based adversarial success rates from 82.1% to 18.7% and temporal jitter success rates from 75.8% to 25.1%, while maintaining an energy consumption of approximately 45 microjoules per inference. We report a counter-intuitive reduction in dynamic power consumption in the fully defended configuration, attributed to volatility-gated plasticity mechanisms that induce higher network sparsity. These results provide empirical evidence that neuromorphic sparsity enables sustainable and high-assurance edge autonomy.

翻译：在边缘机器人平台上部署可信人工智能时，需要在高度鲁棒的保障机制与能源可持续性之间进行艰难权衡。传统对抗攻击防御机制通常会产生显著的计算开销，威胁到在受限功率平台（如地月空间环境）上的可行性。本文量化了事件驱动神经形态系统中保障机制的能耗成本。我们在BrainChip Akida AKD1000处理器上，针对一系列对抗性时序攻击对分层时序防御（HTD）框架进行了基准测试。研究表明，与传统深度学习防御方法（其鲁棒性提升往往导致效率显著下降）不同，所提出架构的事件驱动特性实现了更优的权衡。该系统将基于梯度的对抗攻击成功率从82.1%降至18.7%，时序抖动攻击成功率从75.8%降至25.1%，同时保持每次推理约45微焦耳的能耗水平。我们观察到完全防御配置下动态功耗出现反直觉的降低，这归因于波动门控可塑性机制诱导产生的更高网络稀疏性。这些结果为神经形态稀疏性能够实现可持续且高保障的边缘自主性提供了实证依据。