  The increased use of Convolutional Neural Networks (CNN) in mission critical
systems has increased the need for robust and resilient networks in the face of
both naturally occurring faults as well as security attacks. The lack of
robustness and resiliency can lead to unreliable inference results. Current
methods that address CNN robustness require hardware modification, network
modification, or network duplication. This paper proposes MILR a software based
CNN error detection and error correction system that enables self-healing of
the network from single and multi bit errors. The self-healing capabilities are
based on mathematical relationships between the inputs,outputs, and
parameters(weights) of a layers, exploiting these relationships allow the
recovery of erroneous parameters (weights) throughout a layer and the network.
MILR is suitable for plaintext-space error correction (PSEC) given its ability
to correct whole-weight and even whole-layer errors in CNNs.
