神经形态机器人在低功耗下实现高可靠防御，能耗仅45微焦/推理。
Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics
- 采用事件驱动的分层时序防御框架，利用神经形态稀疏性提升效率。
- 对抗攻击成功率从82.1%降至18.7%，时间抖动攻击从75.8%降至25.1%。
- 防御后动态功耗反而下降，适合太空等严苛边缘场景应用。
在边缘机器人上部署可信人工智能面临高保障鲁棒性与能源可持续性之间的艰难权衡。传统对抗攻击防御机制通常带来显著计算开销，威胁如地月空间等供电受限平台的可行性。本文量化了事件驱动神经形态系统中保障的能耗成本。我们在BrainChip Akida AKD1000处理器上对分层时序防御（HTD）框架进行了基准测试，针对一系列对抗性时间攻击。结果表明，与传统深度学习防御不同，所提架构因事件驱动特性实现了更优权衡：梯度攻击成功率由82.1%降至18.7%，时间抖动攻击由75.8%降至25.1%，同时保持约45微焦/推理的能耗。令人意外的是，全防御配置下动态功耗反而降低，归因于波动门控可塑性机制带来的更高网络稀疏性。这些结果为神经形态稀疏性支持可持续、高保障边缘自主提供了实证依据。
原文摘要 · Abstract (English)
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.
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