Hardware Implementation of Neuromorphic Computing Using Large-Scale Memristor Crossbar Arrays

Brain-inspired neuromorphic computing is a new paradigm that holds great potential to overcome the intrinsic energy and speed issues of traditional von Neumann based computing architecture. With the ability to perform vector-matrix multiplications and flexible tunable conductance, the memristor crossbar array (CBA) structure is one of the most promising candidates to realize neural cognitive systems. The boom in the development of memristive synapses and neurons has propelled the developments of artificial neural networks (ANNs) to emulate the highly hierarchically organized network of human brain in the past decade. To achieve this, realizing large scale, high-density memristive CBAs is a prerequisite to constructing complex ANNs. Herein, the stringent requirements in device performance and array parameters for hardware ANNs are analyzed, and the efforts in addressing the associated challenges are discussed. Recent progress on the experimental demonstration of neuromorphic computing systems (NCSs) is presented. Recommendations for further performance optimization at the device, circuit, and algorithm levels are proposed. This Report serves as a guide for the hardware implementation of NCS based on large-scale CBAs.

Automated summary

Brain-inspired neuromorphic computing shows potential in overcoming the energy and speed limitations of traditional von Neumann computing. The use of memristor crossbar arrays is key in constructing complex artificial neural networks and emulating the hierarchical organization of the human brain. Recommendations for further optimization at device, circuit, and algorithm levels are provided for the hardware implementation of neuromorphic computing systems based on large-scale CBAs.