Estimating Redundancy-Reliability of CNNs Based on Strip-Median Attributes

Redundancy-reliability calculation for deep neural networks is of great importance for their lightweight operation. This article proposes a method for calculating the redundancy-reliability of convolutional neural networks (CNNs) based on strip-median attributes. First, based on the common cause principle, an input-sample-oriented reliability calculation method with automatic boundary detection is constructed to measure the network reliability differences caused by normal classification boundary changes. Next, the adaptive clustering algorithm is used and combined with strip-median attributes to identify the redundant strips of each convolutional layer (CONV) in the networks. Then, based on the central limit theorem and the Pauta criterion, a fault injection method with adaptive convergence ability is constructed to effectively calculate the soft-error-oriented redundancy-reliability of CNNs. The tests of multiple widely used CNNs with multiple datasets showed that with the results of the Monte Carlo (MC) model as the references, the reliability calculation accuracy of the proposed method was approximately 0.9834 and was 3.0% higher than that of the logical flips at bit level (LFBL) method. Additionally, the identification accuracy of the proposed method for redundant nodes in CNNs was better than that of the HRank method and the
$1 \times N$
pruning method, by average factors of approximately 14.32 and
$1.55\times $
, respectively. Furthermore, the redundant protection and the adaptive layer geometric center searching mechanism designed by the proposed method were effective. Thus, the proposed method can effectively achieve a one-to-one correspondence between the redundancy and reliability of CNNs.