The emerging metal-oxide resistive switching random-access memory (RRAM) devices and RRAM crossbar arrays have demonstrated their potential in enormously boosting the speed and energy-efficiency of analog matrix-vector multiplication. Unfortunately, due to the immature fabrication technology, commonly occurring Stuck-At-Faults (SAFs) seriously degrade the computational accuracy of RRAM crossbar based Computing System (RCS). In this paper, we propose a Mapping Algorithm with inner fault-tolerant ability (MAO) to convert matrix parameters into RRAM conductances in RCS by providing larger mapping space and fully exploring the available mapping space. Furthermore, we present two computation-oriented redundancy schemes — ‘Redundant Crossbars’ (RX) and ‘Independent Redundant Columns’ (IRC) to alleviate the loss of computational accuracy due to SAFs. RX adds redundant RRAM crossbar arrays and IRC introduces independent redundant RRAM columns to compensate the computational errors brought by SAFs.

Convolutional Neural Network (CNN) is a powerful technique widely used in computer vision area, which also demands much more computations and memory resources than traditional solutions. The emerging metal-oxide resistive random-access memory (RRAM) and RRAM crossbar have shown great potential on neuromorphic applications with high energy efficiency. However, the interfaces between analog RRAM crossbars and digital peripheral functions, namely Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs), consume most of the area and energy of RRAM-based CNN design due to the large amount of intermediate data in CNN. In this paper, we propose an energy efficient structure for RRAM-based CNN. Based on the analysis of data distribution, a quantization method is proposed to transfer the intermediate data into 1 bit and eliminate DACs. An energy efficient structure using input data as selection signals is proposed to reduce the ADC cost for merging results of multiple crossbars. The experimental results show that the proposed method and structure can save 80% area and more than 95% energy while maintaining the same or comparable classification accuracy of CNN on MNIST.