APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors
Mahdi Taheri Mohammad Hasan Ahmadilivani Maksim Jenihhin Masoud Daneshtalab Jaan Raik
Nowadays, the extensive exploitation of Deep Neural Networks (DNNs) in
safety-critical applications raises new reliability concerns. In practice,
methods for fault injection by emulation in hardware are efficient and widely
used to study the resilience of DNN architectures for mitigating reliability
issues already at the early design stages. However, the state-of-the-art
methods for fault injection by emulation incur a spectrum of time-, design- and
control-complexity problems. To overcome these issues, a novel resiliency
assessment method called APPRAISER is proposed that applies functional
approximation for a non-conventional purpose and employs approximate computing
errors for its interest. By adopting this concept in the resiliency assessment
domain, APPRAISER provides thousands of times speed-up in the assessment
process, while keeping high accuracy of the analysis. In this paper, APPRAISER
is validated by comparing it with state-of-the-art approaches for fault
injection by emulation in FPGA. By this, the feasibility of the idea is
demonstrated, and a new perspective in resiliency evaluation for DNNs is
opened.