The advent of High-Performance Computing has led to the adoption of Convolutional Neural Networks (CNNs) in safety-critical applications such as autonomous vehicles. However, CNNs are vulnerable to DRAM errors corrupting their parameters, thereby degrading their accuracy. Existing techniques for protecting CNNs from DRAM errors are either expensive or fail to protect from large-granularity, multi-bit errors, which occur commonly in DRAMs.