Semi-Trained Memristive Crossbar Computing Engine with In Situ Learning Accelerator

Abstract

On-device intelligence is gaining significant attention recently as it offers local data processing and low power consumption. In this research, an on-device training circuitry for threshold-current memristors integrated in a crossbar structure is proposed. Furthermore, alternate approaches of mapping the synaptic weights into fully trained and semi-trained crossbars are investigated. In a semi-trained crossbar, a confined subset of memristors are tuned and the remaining subset of memristors are not programmed. This translates to optimal resource utilization and power consumption, compared to a fully programmed crossbar. The semi-trained crossbar architecture is applicable to a broad class of neural networks. System level verification is performed with an extreme learning machine for binomial and multinomial classification. The total power for a single 4 × 4 layer network, when implemented in IBM 65nm node, is estimated to be ≈42.16μW and the area is estimated to be 26.48μm × 22.35μm.