Presentation
Scalable Reliability Assessment of DNNs Through Simultaneous Fault Injection
DescriptionDeep Neural Networks (DNNs) are being deployed in safety-critical applications, where resilience to transient faults is essential. Traditional fault injection methods often face challenges in scaling efficiently to larger models, whereas the majority of existing speed-up techniques is closely linked to specific hardware or architectural configurations.
To speed up the assessment of single-fault effects, an approach based on simultaneous injection of faults has demonstrated promising results, where multiple non-interacting faults are injected concurrently during a single workload execution. Nevertheless, the applicability of this method to DNNs has not been explored. In this study, we investigate the use of simultaneous injection of faults in DNNs and observe that faults can easily interact with one another due to DNNs' densely connected structure.
These fault interactions can create ``artificial'' masking effects, leading to the misclassification of faults as non-critical (called false negatives), ultimately compromising the accuracy of the reliability assessment.
To overcome this phenomenon, we propose an approach to mitigate the effects of such fault interaction during simultaneous injection of faults in DNNs, ensuring accurate assessment. Furthermore, we propose a strategy to further accelerate the assessment by pruning non-critical inputs from the DNN input batch during fault injection, further improving the speedup with negligible accuracy loss. To our knowledge, this is the first approach to enable accurate and efficient simultaneous injection of faults into DNNs, supporting fast reliability assessment applicable to different abstraction levels. We experiment with nearly 42 million injections at both software (SW) and RTL, achieving very low false negatives (as low as 0%, avg 0.2%) and an average injection time gain of 3.82x (RTL) and 5.29x (SW) over existing DNN fault injection approaches.
To speed up the assessment of single-fault effects, an approach based on simultaneous injection of faults has demonstrated promising results, where multiple non-interacting faults are injected concurrently during a single workload execution. Nevertheless, the applicability of this method to DNNs has not been explored. In this study, we investigate the use of simultaneous injection of faults in DNNs and observe that faults can easily interact with one another due to DNNs' densely connected structure.
These fault interactions can create ``artificial'' masking effects, leading to the misclassification of faults as non-critical (called false negatives), ultimately compromising the accuracy of the reliability assessment.
To overcome this phenomenon, we propose an approach to mitigate the effects of such fault interaction during simultaneous injection of faults in DNNs, ensuring accurate assessment. Furthermore, we propose a strategy to further accelerate the assessment by pruning non-critical inputs from the DNN input batch during fault injection, further improving the speedup with negligible accuracy loss. To our knowledge, this is the first approach to enable accurate and efficient simultaneous injection of faults into DNNs, supporting fast reliability assessment applicable to different abstraction levels. We experiment with nearly 42 million injections at both software (SW) and RTL, achieving very low false negatives (as low as 0%, avg 0.2%) and an average injection time gain of 3.82x (RTL) and 5.29x (SW) over existing DNN fault injection approaches.
Event Type
Research Manuscript
TimeWednesday, July 292:08pm - 2:21pm PDT
LocationMtg Room 101B
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