Enhancing Fault Resilience of QNNs by Selective Neuron Splitting
Mohammad Hasan Ahmadilivani Mahdi Taheri Jaan Raik Masoud Daneshtalab Maksim Jenihhin
The superior performance of Deep Neural Networks (DNNs) has led to their
application in various aspects of human life. Safety-critical applications are
no exception and impose rigorous reliability requirements on DNNs. Quantized
Neural Networks (QNNs) have emerged to tackle the complexity of DNN
accelerators, however, they are more prone to reliability issues.
  In this paper, a recent analytical resilience assessment method is adapted
for QNNs to identify critical neurons based on a Neuron Vulnerability Factor
(NVF). Thereafter, a novel method for splitting the critical neurons is
proposed that enables the design of a Lightweight Correction Unit (LCU) in the
accelerator without redesigning its computational part.
  The method is validated by experiments on different QNNs and datasets. The
results demonstrate that the proposed method for correcting the faults has a
twice smaller overhead than a selective Triple Modular Redundancy (TMR) while
achieving a similar level of fault resiliency.