Framework and Layer-Wise Word-Line Activation Method Design for CIM

Convolutional Neural Networks (CNNs) have excellent performance in various fields, such as machine learning, computer vision, and image recognition. With the development of CNNs, huge quantities of data in computing and transmission have placed significant pressure on circuit and architecture design, and RRAM-based computing-in-memory (CIM) is one of the promising solutions to alleviate this problem. However, because of the current deviation phenomenon and the resistance on/off ratio (R ratio) issue in RRAM, there is a trade-off problem between computational accuracy and computational efficiency for CIM. In this paper, we propose a layer-wise activated word-line (AWL) strategy to configure the appropriate number of AWLs for each layer. Based on the observed risk factors, we design a risk index to AWL mapping methodology. Meanwhile, based on the proposed quantization and current deviation error calculation methods, we design a CIM simulation framework to simulate the accuracy of CNNs in the inference stage. We evaluate our methodology on Cifar-10, VGG-8, and ResNet-18. The proposed methodology improves computational efficiency with only slight accuracy loss. In comparison with a fixed-AWL configuration, our methodology has better accuracy with a small resistance on/off ratio. For higher resistance on/off ratios, our methodology gets a significant improvement in computational efficiency in comparison with the baseline. On the exploration of different R ratios, experimental results show that our layer-wise AWL configuration methodology has a more flexible planning space and better computational efficiency.