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Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics

Sylvester Kaczmarek

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.

Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics

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.
Paper Structure (33 sections, 1 equation, 4 figures, 3 tables)

This paper contains 33 sections, 1 equation, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Neuromorphic Efficiency
  4. Adversarial Robustness at the Edge
  5. Benchmarking Methodologies
  6. System Under Test
  7. Neuromorphic Hardware Platform
  8. Assurance Architecture
  9. Baseline Configuration
  10. Input Assurance Layer
  11. Neuronal Assurance Layer
  12. Synaptic Assurance Layer
  13. Benchmarking Protocol
  14. Dataset and Workload
  15. Threat Model
  16. ...and 18 more sections

Figures (4)

  • Figure 1: The Hierarchical Temporal Defense (HTD) Architecture used for benchmarking. The system integrates three layers of assurance: (1) Input Assurance via Bayesian Spike Pattern Superposition (BSPS), (2) Neuronal Assurance via Homeostatic Adaptive Thresholds, and (3) Synaptic Assurance via Volatility-Gated Metaplasticity. Each layer filters specific temporal adversarial artifacts before they propagate.
  • Figure 2: Efficiency-Security Trade-off. The plot illustrates the relationship between energy consumption and adversarial success rate for the baseline and defended configurations. The trajectory demonstrates that the fully defended model achieves both higher robustness and lower energy consumption due to induced sparsity.
  • Figure 3: Sensitivity Analysis. The plots show the Adversarial Success Rate (ASR) as a function of attack strength for (a) PGD and (b) Temporal Jitter. The defended model exhibits significantly lower vulnerability and a more graceful degradation profile compared to the baseline.
  • Figure 4: Energy vs. Normalized Activity. The plot demonstrates a strong linear correlation between the total spike activity in the network and the measured energy consumption. The Full Defense configuration achieves the lowest energy by suppressing volatile synaptic activity, thereby reducing the overall event rate.