Fault tolerance in memristive crossbar-based neuromorphic computing systems

Abstract

In recent years, neuromorphic computing systems (NCS) based on memristive crossbar have provided a promising solution to enable acceleration of neural networks. However, Stuck-at faults in the memristor devices significantly degrade the computing accuracy of NCS. In this paper, we propose an effective fault tolerant framework for memristive crossbar-based neuromorphic computing systems. First, a fault tolerance-aware hierarchical clustering method is proposed to partition weight connections of a sparse neural network into clusters. Then, for each cluster, memristive crossbar configuration is proposed to determine a suitable size of the crossbar with consideration of both hardware cost and successful mapping rate. Next, an integer linear programming formulation is developed to derive a connection-memristor mapping for fault tolerance. Finally, an efficient matching-based heuristic algorithm is further proposed to speed-up the fault-tolerant mapping process. Experimental results show that the proposed fault tolerant framework can improve the successful mapping rate and simultaneously reduce the hardware cost.