A Closed-Loop Remapping Algorithm for Memristor-Based Crossbar with a Constraint on the Number of Reprogramming Cells

Memristor-based crossbar can effectively accelerate matrix-vector multiplication in neural network. However, the accuracy of crossbar may seriously decrease due to aging. Memristor-based crossbar will not meet the requirements of accuracy and cannot be used. To continue using the crossbar, this paper proposes a closed-loop remapping algorithm with a constraint on the number of reprogramming cells. Firstly, the row deviation matrix is obtained according to the aging distribution of memristor-based crossbar. The remapping algorithm starts from the minimum value of row deviation matrix until a closed-loop is formed. With the row deviation constraint during remapping process, the sum of row deviations is as small as possible to improve accuracy. Besides, the number of reprogramming cells can be greatly reduced to alleviate aging of crossbar with a constraint on reprogramming cells. Experimental results show that the proposed technique can effectively improve accuracy. Compared with other techniques, it can also reduce the number of reprogramming cells by up to 75.43% while achieving the same accuracy.