1 citations · 1 across the 4 of their papers we have counts for
7 papers
You Only Charge Once 2.0 : A End-to-End Analog Computing-in-Memory Architecture with Reconfigurable Switched Capacitors
Zihao Xuan, Yewen Li, Jia Chen +3
Analog Computing-in-Memory (ACiM) accelerates deep neural networks by keeping weights inside memory arrays and executing dot products in the analog domain. However, modern ACiM acc…
31.1 A 14.08-to-135.69Token/s ReRAM-on-Logic Stacked Outlier-Free Large-Language-Model Accelerator with Block-Clustered Weight-Compression and Adaptive Parallel-Speculative-Decoding
Pingcheng Dong, Yonghao Tan, Xuejiao Liu +13
This work presents a 55nm speculative decoding-based LLM accelerator with bumping-based face-to-face ReRAM-on-logic stacking technology. It features a local rotation unit for outli…
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…
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…
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…
CompAir: Synergizing Complementary PIMs and In-Transit NoC Computation for Efficient LLM Acceleration
Hongyi Li, Songchen Ma, Huanyu Qu +5
The rapid advancement of Large Language Models (LLMs) has revolutionized various aspects of human life, yet their immense computational and energy demands pose significant challeng…