| dc.description.abstract | Deep Neural Networks (DNNs) have become indispensable in bolstering security-sensitive systems such as autonomous vehicles and medical monitoring. However, their reliability in the face of hardware-induced errors, particularly aging effects, remains a pressing concern. This work conducts a thorough investigation into the repercussions of aging effects, including Negative Bias Temperature Instability (NBTI), Positive Bias Temperature Instability (PBTI), and Hot Carrier Injection (HCI), on DNN reliability while proposing practical solutions to counteract these challenges.
We conduct a comprehensive analysis of aging effects within memory devices, involving simulations to replicate aging conditions, observation of aging-induced errors (AIE), and their subsequent application to DNNs. Through this examination, we identified the detrimental impact of aging-induced errors on DNN accuracy. To address this, we study effective remedies, such as redundant bit injection (RBI) and lowering operation frequency (LOF). The RBI method, requiring only 25% redundant bits, significantly sustained DNN performance, while the LOF method minimized performance degradation to just 1% timing overhead. These remedies offer practical solutions to mitigate accuracy degradation caused by aging effects, thereby enhancing the reliability of DNNs in real-world applications.
Through meticulous experimentation and analysis, we uncover the deterministic nature of AIE and propose strategies to enhance DNN resilience. In addition to introducing the two methods mentioned above to minimize the impact of aging effects on DNN accuracy, we also provide some possible future development directions, providing valuable insights for preserving model integrity in real-world applications, particularly in safety-critical and security-sensitive domains. Our findings underscore the critical importance of proactive measures to bolster DNN resilience, laying the groundwork for future research endeavors aimed at ensuring the steadfastness of DNNs in the face of evolving challenges. | en_US |