Cao, T., Liu, C., Gao, Y., & Goh, W. L. (2021). Parasitic-Aware Modelling for Neural Networks Implemented with Memristor Crossbar Array. 2021 IEEE 14th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSoC). https://doi.org/10.1109/mcsoc51149.2021.00025
This paper presents a parasitic-aware modelling approach called αβ-matrix model for the simulation of neural network (NN) implemented with memristor crossbar array. The line resistance, which is the key parasitic in a memristor crossbar array is analyzed and incorporated into the model. The proposed method estimates the line resistance IR drop with computation complexity of O(mn), in contrast to O(m 2 n 2 ) required by the classical matrix based Kirchhoff's Current Law (KCL) equations solver. The impact of the crossbar array parasitics to the vector-matrix multiplication (VMM) computation and multi-layer NN classification accuracy are also analyzed. The advantages of the proposed parasitic-aware model are demonstrated through an example of 2-layer perceptron implemented with resistive random access memory (RRAM) crossbar array for MNIST written digits classification. 97.3% classification accuracy is achieved on 64×64 6-bit RRAM crossbar arrays. Compared to the KCL solver, the classification accuracy degradation is less than 0.4% with line resistance up to 4.5Ω.
This research / project is supported by the A*STAR - Programmatic - Nanosystems at the Edge
Grant Reference no. : A18A4b0055