Memristor crossbar based hardware realization of BSB recall function

The Brain-State-in-a-Box (BSB) model is an auto-associative neural network that has been widely used in optical character recognition and image processing. Traditionally, the BSB model was realized at software level and carried out on high-performance computing clusters. To improve computation efficiency and reduce resource requirement, we propose a hardware realization by utilizing memristor crossbar arrays. Memristors can remember the historical profiles of the excitations and record them as analog variables. The similarity to biological synaptic behavior has encouraged a lot of research on memristor-based neuromorphic hardware system. In this work, we explore the potential of a memristor crossbar array as an auto-associative memory. More specifically, the recall function of a multi-answer character recognition based on BSB model was realized. The robustness of the proposed BSB circuit was analyzed and evaluated based on massive Monte-Carlo simulations, considering input defects, process variations, and electrical fluctuations. The physical constraints when implementing a neural network with memristor crossbar array have also been discussed. Our results show that the BSB circuit has a high tolerance to random noise. Comparably, the correlations between memristor arrays introduce directional noise and hence dominate the quality of the circuit.