Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics

TL;DR

This paper evaluates the energy efficiency of neuromorphic systems implementing assurance mechanisms in edge robotics, showing they can maintain robustness with lower energy costs compared to traditional methods.

Contribution

It introduces a benchmarking framework for energy cost of assurance in neuromorphic edge systems and demonstrates superior trade-offs between robustness and energy efficiency.

Findings

Significant reduction in adversarial success rates with low energy overhead.

Neuromorphic architecture maintains high robustness while reducing power consumption.

Sparsity mechanisms contribute to lower dynamic power in defended systems.

Abstract

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.