3 papers
cs.MA2026
DynaGraph: Lightweight Multi-Model Interaction Framework via Dynamic Topological Reconfiguration
Yanxing Guo, Zihao Zheng, Fangzhou Wu +4
Tackling complex reasoning tasks typically relies on massive monolithic LLMs, which suffer from severe computational redundancy. While task decomposition through structured pipelin…
cs.AR2026
RCW-CIM: A Digital CIM-based LLM Accelerator with Read-Compute/Write
Yan-Cheng Guo, Tian-Sheuan Chang, Jian-Wei Su
Digital computing-in-memory (DCIM) has emerged as a promising solution for large language model (LLM) acceleration by minimizing data transfers between external DRAM and on-chip ac…
cs.AR2025
CIMR-V: An End-to-End SRAM-based CIM Accelerator with RISC-V for AI Edge Device
Yan-Cheng Guo and, Tian-Sheuan Chang, Chih-Sheng Lin +5
Computing-in-memory (CIM) is renowned in deep learning due to its high energy efficiency resulting from highly parallel computing with minimal data movement. However, current SRAM-…