An RRAM-Based Computing-in-Memory Macro With Low-Power Readout/Hold Circuits and Activation Differential Strategy for AdderNet

作者：Zhihang Qian, Shengzhe Yan, Zhuoyu Dai, Zeyu Guo, Zhaori Cong, Yifan He, Chunmeng Dou, Feng Zhang, Jinshan Yue, Yongpan Liu · 发表于：IEEE Transactions on Very Large Scale Integration (VLSI) Systems · 年份：2025 · DOI：10.1109/tvlsi.2025.3546684 · 被引用次数：4 · 研究领域：Advanced Memory and Neural Computing、Ferroelectric and Negative Capacitance Devices、Machine Learning and ELM

AdderNet is an innovative neural network (NN) structure that substitutes multiplications with additions in convolutional operations, while computing-in-memory (CIM) is an efficient architecture that tackles the memory bottleneck for von Neumann architectures. Previous work has explored the SRAM-based CIM AdderNet circuits and demonstrates high energy efficiency. However, it still suffers low storage density, repetitive readout, and redundant comparisons. In this brief, an RRAM-based CIM macro is proposed for efficient AdderNet with the following innovations. First, RRAM cells are adopted to replace SRAM for high-density weight storage. A low-power readout and hold circuit is proposed to save redundant read power of weight data held for multiple cycles. Second, an 8-bit comparator with an early-stop strategy is proposed to compare 8-bit activations and weights in one cycle. Third, an activation (ACT) differential strategy is proposed to reduce redundant comparisons. The proposed 28-nm RRAM CIM macro achieves 12.8-TOPS/mm2peak area efficiency and 126-TOPS/W peak energy efficiency, which is$3.0\times $and$1.2\times $compared with the state-of-the-art AdderNet CIM macro.