  Deep neural networks have demonstrated state-of-the-art performance on many
classification tasks. However, they have no inherent capability to recognize
when their predictions are wrong. There have been several efforts in the recent
past to detect natural errors but the suggested mechanisms pose additional
energy requirements. To address this issue, we propose an ensemble of
classifiers at hidden layers to enable energy efficient detection of natural
errors. In particular, we append Relevant-features based Auxiliary Cells (RACs)
which are class specific binary linear classifiers trained on relevant
features. The consensus of RACs is used to detect natural errors. Based on
combined confidence of RACs, classification can be terminated early, thereby
resulting in energy efficient detection. We demonstrate the effectiveness of
our technique on various image classification datasets such as CIFAR-10,
CIFAR-100 and Tiny-ImageNet.
