# SoftSNN: Low-Cost Fault Tolerance for Spiking Neural Network Accelerators under Soft Errors

arXiv: https://arxiv.org/abs/2203.05523 · HTML: https://mlsanity.com/paper/2203.05523/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [BindsNET: A machine learning-oriented spiking neural networks library in Python](https://mlsanity.com/paper/1806.01423/) (2018)
- [FSpiNN: An Optimization Framework for Memory- and Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2007.08860/) (2020)
- [SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM](https://mlsanity.com/paper/2103.00421/) (2021)
- [ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories](https://mlsanity.com/paper/2108.10271/) (2021)
- [On the Self-Repair Role of Astrocytes in STDP Enabled Unsupervised SNNs](https://mlsanity.com/paper/2009.03473/) (2020)

## Built on by (most cited first, 18 of 18)

- [lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents](https://mlsanity.com/paper/2205.12295/) (2022)
- [SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Embedded AI Systems](https://mlsanity.com/paper/2402.11322/) (2024)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [QSViT: A Methodology for Quantizing Spiking Vision Transformers](https://mlsanity.com/paper/2504.00948/) (2025)
- [Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack](https://mlsanity.com/paper/2404.03325/) (2024)
- [EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems](https://mlsanity.com/paper/2304.04039/) (2023)
- [RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults](https://mlsanity.com/paper/2304.04041/) (2023)
- [Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks](https://mlsanity.com/paper/2212.12620/) (2022)
- [Input-Triggered Hardware Trojan Attack on Spiking Neural Networks](https://mlsanity.com/paper/2503.21793/) (2025)
- [enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks](https://mlsanity.com/paper/2208.00328/) (2022)
- [SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous Agents](https://mlsanity.com/paper/2404.09331/) (2024)
- [Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems](https://mlsanity.com/paper/2504.00957/) (2025)
- [tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2206.08656/) (2022)
- [SpikeVox: Towards Energy-Efficient Speech Therapy Framework with Spike-driven Generative Language Models](https://mlsanity.com/paper/2510.15566/) (2025)
- [PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers](https://mlsanity.com/paper/2606.03257/) (2026)
- [PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers](https://mlsanity.com/paper/2606.03428/) (2026)
- [BLADE: ReliaBle Dynamic Hardware-Aware SNN-ANN Boundary SeLection for Event-BAseD Object DEtection](https://mlsanity.com/paper/2609.17562/) (2026)
- [MorphAtt: A Neuromorphic Accelerator for Efficient Multi-Head Attention Processing in Spiking Vision Transformers](https://mlsanity.com/paper/2609.33207/) (2026)

## Abstract (arXiv)

Specialized hardware accelerators have been designed and employed to maximize the performance efficiency of Spiking Neural Networks (SNNs). However, such accelerators are vulnerable to transient faults (i.e., soft errors), which occur due to high-energy particle strikes, and manifest as bit flips at the hardware layer. These errors can change the weight values and neuron operations in the compute engine of SNN accelerators, thereby leading to incorrect outputs and accuracy degradation. However, the impact of soft errors in the compute engine and the respective mitigation techniques have not been thoroughly studied yet for SNNs. A potential solution is employing redundant executions (re-execution) for ensuring correct outputs, but it leads to huge latency and energy overheads. Toward this, we propose SoftSNN, a novel methodology to mitigate soft errors in the weight registers (synapses) and neurons of SNN accelerators without re-execution, thereby maintaining the accuracy with low latency and energy overheads. Our SoftSNN methodology employs the following key steps: (1) analyzing the SNN characteristics under soft errors to identify faulty weights and neuron operations, which are required for recognizing faulty SNN behavior; (2) a Bound-and-Protect technique that leverages this analysis to improve the SNN fault tolerance by bounding the weight values and protecting the neurons from faulty operations; and (3) devising lightweight hardware enhancements for the neural hardware accelerator to efficiently support the proposed technique. The experimental results show that, for a 900-neuron network with even a high fault rate, our SoftSNN maintains the accuracy degradation below 3%, while reducing latency and energy by up to 3x and 2.3x respectively, as compared to the re-execution technique.