SpikingJET: A Comprehensive Fault Injection Framework for Evaluating the Resilience of Spiking Neural Networks

Concepts de base

SpikingJET is a novel fault injector designed specifically for fully connected and convolutional spiking neural networks, enabling comprehensive assessment of their resilience to hardware faults.

Résumé

The paper introduces SpikingJET, a fault injection framework for evaluating the resilience of spiking neural networks (SNNs) to hardware faults.

Key highlights:

• SpikingJET can inject faults into critical SNN components such as synaptic weights, neuron model parameters, internal states, and activation functions.
• The tool supports three primary fault models: stuck-at faults affecting synaptic connections, byzantine neurons with timing variations, and crashed neurons.
• Extensive software-level experiments were conducted on three SNN architectures (fully connected, recurrent fully connected, and convolutional) using three benchmark datasets (N-MNIST, Spiking Heildelberg Digits, and DVS 128 gestures).
• The results reveal insights into the vulnerability and resilience of different SNN models and components to hardware faults.
• The analysis shows that most faults are masked, indicating the inherent resilience of SNNs. However, faults in outer layers and certain parameters like thresholds and output spikes have a higher impact.
• The study highlights the importance of fault resilience in SNNs and contributes to the ongoing effort to enhance the reliability and safety of neural network-powered systems.

Stats

The total number of injected faults and the time required for the complete fault injection campaign are as follows:

DVS128: 16,307 faults, 1:42:25 hours
N-MNIST: 15,944 faults, 4:59:06 hours
SHD: 16,578 faults, 1:09:44 hours

Citations

"As artificial neural networks become increasingly integrated into safety-critical systems such as autonomous vehicles, devices for medical diagnosis, and industrial automation, ensuring their reliability in the face of random hardware faults becomes paramount."
"SpikingJET provides a comprehensive platform for assessing the resilience of SNNs by inducing errors and injecting faults into critical components such as synaptic weights, neuron model parameters, internal states, and activation functions."
"The injection campaign targets three SNN models, evaluated on as many benchmark datasets: N-MNIST, Spiking Heildelberg Digits (SHD) and Dynamic Vision Sensor (DVS) 128 gestures, considering Convolutional, Feed Forward (FF) and recurrent Fully Connected (FC) architectures."

Idées clés tirées de

SpikingJET

by Anil Bayram ... à arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00383.pdf

Questions plus approfondies

How can the fault injection capabilities of SpikingJET be extended to include other fault models, such as transient faults or multi-bit errors, to provide a more comprehensive assessment of SNN resilience?

To enhance the fault injection capabilities of SpikingJET and provide a more comprehensive assessment of Spiking Neural Network (SNN) resilience, the tool can be extended to incorporate various fault models beyond stuck-at faults.

Transient Faults:

Modeling: Introduce mechanisms to simulate transient faults that cause temporary disruptions in the system's behavior. This can involve altering parameters dynamically during inference to mimic transient effects.
Injection Points: Identify critical points in the SNN where transient faults can have a significant impact, such as during spike propagation or integration stages.

Multi-Bit Errors:

Parameter Corruption: Extend the fault injection framework to introduce multi-bit errors in synaptic weights, neuron states, or activation functions simultaneously.
Impact Analysis: Develop methods to analyze the effects of multi-bit errors on SNN performance and resilience, considering the complex interactions between multiple faulty components.

Statistical Fault Injection:

Error Estimation: Implement statistical fault injection techniques to estimate the impact of transient faults or multi-bit errors on SNNs with a defined error margin and confidence level.
Sample Generation: Generate fault samples representative of the entire fault universe to ensure a thorough exploration of fault scenarios.

By incorporating these fault models and techniques into SpikingJET, researchers and developers can conduct more in-depth assessments of SNN resilience under a broader range of fault conditions, leading to a more robust evaluation of the network's reliability in real-world applications.

How can the fault injection capabilities of SpikingJET be extended to include other fault models, such as transient faults or multi-bit errors, to provide a more comprehensive assessment of SNN resilience?

To enhance the fault injection capabilities of SpikingJET and provide a more comprehensive assessment of Spiking Neural Network (SNN) resilience, the tool can be extended to incorporate various fault models beyond stuck-at faults.

Transient Faults:

Modeling: Introduce mechanisms to simulate transient faults that cause temporary disruptions in the system's behavior. This can involve altering parameters dynamically during inference to mimic transient effects.
Injection Points: Identify critical points in the SNN where transient faults can have a significant impact, such as during spike propagation or integration stages.

Multi-Bit Errors:

Parameter Corruption: Extend the fault injection framework to introduce multi-bit errors in synaptic weights, neuron states, or activation functions simultaneously.
Impact Analysis: Develop methods to analyze the effects of multi-bit errors on SNN performance and resilience, considering the complex interactions between multiple faulty components.

Statistical Fault Injection:

Error Estimation: Implement statistical fault injection techniques to estimate the impact of transient faults or multi-bit errors on SNNs with a defined error margin and confidence level.
Sample Generation: Generate fault samples representative of the entire fault universe to ensure a thorough exploration of fault scenarios.

By incorporating these fault models and techniques into SpikingJET, researchers and developers can conduct more in-depth assessments of SNN resilience under a broader range of fault conditions, leading to a more robust evaluation of the network's reliability in real-world applications.

How can the fault injection capabilities of SpikingJET be extended to include other fault models, such as transient faults or multi-bit errors, to provide a more comprehensive assessment of SNN resilience?

To enhance the fault injection capabilities of SpikingJET and provide a more comprehensive assessment of Spiking Neural Network (SNN) resilience, the tool can be extended to incorporate various fault models beyond stuck-at faults.

Transient Faults:

Modeling: Introduce mechanisms to simulate transient faults that cause temporary disruptions in the system's behavior. This can involve altering parameters dynamically during inference to mimic transient effects.
Injection Points: Identify critical points in the SNN where transient faults can have a significant impact, such as during spike propagation or integration stages.

Multi-Bit Errors:

Parameter Corruption: Extend the fault injection framework to introduce multi-bit errors in synaptic weights, neuron states, or activation functions simultaneously.
Impact Analysis: Develop methods to analyze the effects of multi-bit errors on SNN performance and resilience, considering the complex interactions between multiple faulty components.

Statistical Fault Injection:

Error Estimation: Implement statistical fault injection techniques to estimate the impact of transient faults or multi-bit errors on SNNs with a defined error margin and confidence level.
Sample Generation: Generate fault samples representative of the entire fault universe to ensure a thorough exploration of fault scenarios.

By incorporating these fault models and techniques into SpikingJET, researchers and developers can conduct more in-depth assessments of SNN resilience under a broader range of fault conditions, leading to a more robust evaluation of the network's reliability in real-world applications.