A High Throughput Generative Vector Autoregression Model for Stochastic Synapses

Recent trends in computing hardware have placed increasing emphasis on neuromorphic architectures implementing machine learning (ML) algorithms directly in hardware. Such bio-inspired approaches, through in-memory computation and massive parallelism, excel in new classes of computational problems and offer promising advantages with respect to power consumption error resiliency. While CMOS-based neuromorphic computing (NC) implementations have made substantial progress recently, new materials and physical mechanisms may ultimately provide better opportunities for energy efficiency and scaling [1, 2, 3]. A specific functionality required in NC applications is the ability to mimic synaptic connections and plasticity by allowing the storage of large numbers of interconnected and continuously adaptable resistance values. Several candidate memory technologies such as MRAM, ReRAM, PCM, CeRAM, are emerging to cover this behavior using different physical mechanisms [4, 5, 6, 7]. Among these, ReRAM is attractive for its simplicity of materials and device structure, providing the necessary CMOS compatibility and scalability [8]. ReRAM is essentially a two terminal nanoscale electrochemical cell, whose variable resistance state is based on manipulation of the point defect configuration in the oxide material (depicted in Figure 1).