As artificial neural networks have 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. This paper introduces SpikingJET, a novel fault injector designed specifically for fully connected and convolutional Spiking Neural Networks (SNNs). Our work underscores the critical need to evaluate the resilience of SNNs to hardware faults, considering their growing prominence in real-world applications. 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. This paper demonstrates the effectiveness of SpikingJET through extensive software-level experiments on various SNN architectures, revealing insights into their vulnerability and resilience to hardware faults. Moreover, highlighting the importance of fault resilience in SNNs contributes to the ongoing effort to enhance the reliability and safety of Neural Network (NN)-powered systems in diverse domains.
SpikingJET: Enhancing Fault Injection for Fully and Convolutional Spiking Neural Networks / Göğebakan, Anıl Bayram; Magliano, Enrico; Carpegna, Alessio; Ruospo, Annachiara; Savino, Alessandro; Carlo, Stefano Di. - ELETTRONICO. - (2024), pp. 1-7. (Intervento presentato al convegno 2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS) tenutosi a Rennes (FRA) nel 03-05 July 2024) [10.1109/iolts60994.2024.10616060].
SpikingJET: Enhancing Fault Injection for Fully and Convolutional Spiking Neural Networks
Magliano, Enrico;Carpegna, Alessio;Ruospo, Annachiara;Savino, Alessandro;Carlo, Stefano Di
2024
Abstract
As artificial neural networks have 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. This paper introduces SpikingJET, a novel fault injector designed specifically for fully connected and convolutional Spiking Neural Networks (SNNs). Our work underscores the critical need to evaluate the resilience of SNNs to hardware faults, considering their growing prominence in real-world applications. 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. This paper demonstrates the effectiveness of SpikingJET through extensive software-level experiments on various SNN architectures, revealing insights into their vulnerability and resilience to hardware faults. Moreover, highlighting the importance of fault resilience in SNNs contributes to the ongoing effort to enhance the reliability and safety of Neural Network (NN)-powered systems in diverse domains.File | Dimensione | Formato | |
---|---|---|---|
SpikingJET_Enhancing_Fault_Injection_for_Fully_and_Convolutional_Spiking_Neural_Networks.pdf
accesso riservato
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
334.42 kB
Formato
Adobe PDF
|
334.42 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Paper_IOLTS_2024___AI_THREATS_WS___SpikingJET.pdf
accesso aperto
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
Pubblico - Tutti i diritti riservati
Dimensione
270.09 kB
Formato
Adobe PDF
|
270.09 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2992206