Forming-Free Resistive Switching Memory Crosspoint Arrays for In-Memory Machine Learning

In-memory computing (IMC) with crosspoint arrays of resistive switching memory (RRAM) has gained wide attention for accelerating machine learning, data analysis, and deep neural networks. By IMC, matrix-vector multiplication (MVM) can be executed in the crosspoint array in just one step, thus accelerating a broad range of tasks in machine learning and data analytics. However, a key issue for RRAM crosspoint arrays is the forming operation of the memories which limits the stability and accuracy of the conductance state in the memory device. In this work, a hardware implementation of crosspoint array of forming-free devices for fast, energy-efficient accelerators of MVM is reported. RRAM devices with a 1.5 nm-thick HfO2 layer show an initial low resistance without forming and an analogue-mode programming behavior for high-accuracy IMC. Accurate hardware MVM is demonstrated by experimental eigenvalue/eigenvector calculation according to the power-iteration algorithm, with a fast convergence within about ten iterations to the correct solution. Deflation technique and principal component analysis (PCA) enable the classification of the Iris dataset with 98% accuracy compared with floating-point implementation. These results support forming-free crosspoint arrays for accelerating advanced machine learning with IMC.