Reconfigurable Digital RRAM Logic Enables In-Situ Pruning and Learning for Edge AI

Songqi Wang; Yue Zhang; Jia Chen; Xinyuan Zhang; Yi Li; Ning Lin; Yangu He; Jichang Yang; Yingjie Yu; Zhongrui Wang; Xiaojuan Qi; Han Wang

arXiv · 2506.13151

Reconfigurable Digital RRAM Logic Enables In-Situ Pruning and Learning for Edge AI

Songqi Wang·Yue Zhang·Jia Chen·Xinyuan Zhang·Yi Li·Ning Lin·Yangu He·Jichang Yang·Yingjie Yu·Zhongrui Wang·Xiaojuan Qi·Han Wang

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Abstract

The human brain simultaneously optimizes synaptic weights and topology by growing, pruning, and strengthening synapses while performing all computation entirely in memory. In contrast, modern artificial-intelligence systems separate weight optimization from topology optimization and depend on energy-intensive von Neumann architectures. Here, we present a software-hardware co-design that bridges this gap. On the algorithmic side, we introduce a real-time dynamic weight-pruning strategy that monitors weight similarity during training and removes redundancies on the fly, reducing operations by 26.80% on MNIST and 59.94% on ModelNet10 without sacrificing accuracy (91.44% and 77.75%, respectively). On the hardware side, we fabricate a reconfigurable, fully digital compute-in-memory (CIM) chip based on 180 nm one-transistor-one-resistor (1T1R) RRAM arrays. Each array embeds flexible Boolean logic (NAND, AND, XOR, OR), enabling both convolution and similarity evaluation inside memory and eliminating all ADC/DAC overhead. The digital design achieves zero bit-error, reduces silicon area by 72.30% and overall energy by 57.26% compared to analogue RRAM CIM, and lowers energy by 75.61% and 86.53% on MNIST and ModelNet10, respectively, relative to an NVIDIA RTX 4090. Together, our co-design establishes a scalable brain-inspired paradigm for adaptive, energy-efficient edge intelligence in the future.

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BibTeX RIS

Songqi Wang, Yue Zhang, Jia Chen, Xinyuan Zhang, Yi Li, Ning Lin, Yangu He, Jichang Yang, Yingjie Yu, Zhongrui Wang, Xiaojuan Qi, Han Wang. 2025-06-16. Reconfigurable Digital RRAM Logic Enables In-Situ Pruning and Learning for Edge AI. https://arxiv.org/abs/2506.13151

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