TIENOS

Abstract

This thesis asks whether spiking neural networks (SNNs) and neuromorphic computing constitute a promising alternative to present-day artificial neural networks (ANNs) for autonomous space missions. Focusing on a resource- and power-constrained 1U CubeSat transiting the Van Allen radiation belts, TIENOS is a toolchain that injects radiation-inspired perturbations into trained models and records the layer-specific reactions.

The framework systematically emulates dominant soft-error mechanisms by applying (i) bit-flip faults representative of single-event upsets, (ii) additive Gaussian noise as a proxy for thermal/analog variability, and (iii) dropout-style masking to approximate transient loss or zeroing of activations. Using MNIST (frame-based) and N-MNIST (event-based) benchmarks, we compare LeNet-5–style convolutional neural networks and size-matched multilayer perceptrons with their spiking counterparts to establish an ideal software training baseline. The tool produces per-layer vulnerability profiles and robustness heatmaps across a broad range of perturbation rates, quantifies activity sparsity in SNNs, and can be used to evaluate noise-aware retraining to improve robustness without any overhead, with a path towards on-chip protections such as selective redundancy (such as triple-modular redundancy for neuron parameters), ECC and scrubbing.

Results show that fragility concentrates in a limited subset of layers depending on the fault mechanism, enabling targeted hardening with modest cost. It also indicates that noise-aware retraining improves tolerance without prohibitive accuracy loss and that SNN sparsity yields favourable energy–robustness trade-offs for bursty, event-driven sensing typical of small spacecraft. In this study, the noise-aware learned weights used for inference by the two-tinyODIN setup deliver a 15% higher accuracy for up to 2% of bit-flips in the system. Hybrid ANN–SNN pipelines could further enlarge this envelope by deploying spiking computation where sparsity is highest while retaining dense processing elsewhere, acknowledging that the scalability and baseline power of current neuromorphic platforms remain practical constraints. Overall, the methodology translates environmental assumptions for a 1U CubeSat in the Van Allen belts into actionable, layer-level design rules, providing a principled basis for space-grade, energy-efficient digital SNN accelerators and an open, extensible tool to localise and mitigate radiation-induced vulnerabilities.