11 citations · 11 across the 5 of their papers we have counts for
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cs.AR2026
FusionCIM: Accelerating LLM Inference with Fusion-Driven Computing-in-Memory Architecture
Zihao Xuan, Jia Chen, Yewen Li +4
In this paper, we propose FusionCIM, an operator-fusion-driven compute-in-memory (CIM) accelerator architecture for efficient and scalable LLM inference, with three key innovations…
cs.AI2026
Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement
Wenji Fang, Yao Lu, Shang Liu +5
Recent advances in large language models (LLMs) have sparked growing interest in automatic RTL optimization for better performance, power, and area (PPA). However, existing methods…
eess.IV2026★ 11 cited
A 28nm 0.22μJ/token memory-compute-intensity-aware CNN-Transformer accelerator with hybrid-attention-based layer-fusion and cascaded pruning for semantic-segmentation
Pingcheng Dong, Yonghao Tan, Xuejiao Liu +14
This work presents a 28nm 13.93mm2 CNN-Transformer accelerator for semantic segmentation, achieving 3.86-to-10.91x energy reduction over previous designs. It features a hybrid atte…