13 citations · 14 across the 9 of their papers we have counts for
5 papers · 1 filter
LLMET: Enabling Cross-Layer Evaluation of Emerging M3D Memories for Energy-Efficient LLM Serving
Ming-Yen Lee, Hanchen Yang, Faaiq Waqar +4
The energy consumption of Large Language Model (LLM) serving is becoming a major system challenge as deployment scales, driven by hardware power and thermal constraints and rising…
Hardware Acceleration of Kolmogorov-Arnold Network (KAN) in Large-Scale Systems
Wei-Hsing Huang, Jianwei Jia, Yuyao Kong +4
Recent developments have introduced Kolmogorov-Arnold Networks (KAN), an innovative architectural paradigm capable of replicating conventional deep neural network (DNN) capabilitie…
Architecting Long-Context LLM Acceleration with Packing-Prefetch Scheduler and Ultra-Large Capacity On-Chip Memories
Ming-Yen Lee, Faaiq Waqar, Hanchen Yang +3
Long-context Large Language Model (LLM) inference faces increasing compute bottlenecks as attention calculations scale with context length, primarily due to the growing KV-cache tr…
A3D-MoE: Acceleration of Large Language Models with Mixture of Experts via 3D Heterogeneous Integration
Wei-Hsing Huang, Janak Sharda, Cheng-Jhih Shih +6
Conventional large language models (LLMs) are equipped with dozens of GB to TB of model parameters, making inference highly energy-intensive and costly as all the weights need to b…
Hardware Acceleration of Kolmogorov-Arnold Network (KAN) for Lightweight Edge Inference
Wei-Hsing Huang, Jianwei Jia, Yuyao Kong +4
Recently, a novel model named Kolmogorov-Arnold Networks (KAN) has been proposed with the potential to achieve the functionality of traditional deep neural networks (DNNs) using or…