The Impact of Non-linear NVM Devices on In-Memory Computing

Deep learning has significantly improved the accuracy of large-scale visual/auditory recognition and classification tasks, at the cost of ever-increasing computational resource and storage capacity in hardware. As a result, the data communication between the computing and storage units has been the bottleneck in Artificial Intelligence (AI) computation. The emerging resistive NVMs based in-memory computing architectures have been considered at the promising solution scheme to address the abovementioned issue. However, the non-linearity of the NVM devices has a significant impact on the computing accuracy. In this paper, a non-linear RRAM is modelled and implemented in various in-memory computing architectures. The results show severe accuracy losses caused by the non-linear reading/writing property, mismatch, uncertainty, etc. Several promising solutions are also discussed in this paper.