A Hierarchical Fault-Tolerant and Cost Effective Framework for RRAM Based Neural Computing Systems

An RRAM-based computing system (RCS) is widely used in neuromorphic computing systems due to its fast computation and low cost. The immature fabrication processes cause high rate of hard faults and limited endurance of RRAMs restrict the life of RCS. These can degrade the accuracy of RCS remarkably. The training of neural network consumes lot of time due to large network size and more number of layers. We propose a hierarchical fault-tolerant and cost effective framework for RCS. The proposed flow can tolerate the faults in the next training phase using the inherent fault-tolerant capability of the neural network without using extra hardware. Proposed framework consists of significant classification and remapping, transfer learning, threshold training and online fault detection. The proposed solution is validated and evaluated on CIFAR-10 dataset on VGG11 and Alexnet networks. Proposed solution resulted in training time improvements of 40% and number of RRAM writes are reduced by 97%.