Parasitic-Aware Modeling and Neural Network Training Scheme for Energy-Efficient Processing-in-Memory With Resistive Crossbar Array

This paper presents a parasitic-aware modeling method for fast and accurate simulation of Processing-in-Memory (PIM) neural network (NN) implemented in resistive memristor crossbar array. This work proposed an efficient and accurate crossbar line resistance estimation model named $\gamma $ -compact model, and the associated NN training scheme that takes the impact of line resistance into consideration with time complexity of O(mn) for a $\text{m}\times \text{n}$ resistive crossbar. The impact of the crossbar array parasitics to the vector-matrix multiplication (VMM) computation accuracy and multi-layer PIM NN inference accuracy are analyzed in detail. The advantage of the proposed model is demonstrated by a multilayer perceptron (MLP) of size $784\times 128\times 10$ implemented with resistive random access memory (RRAM) crossbar arrays for MNIST hand-written digits classification. The proposed method reduced the VMM computation error by 186 and 17 times compared to uncompensated method and the state-of-art compensation method, respectively. Maximum 98.1% inference accuracy is achieved with only 0.17% degradation compared to the ideal model.