6 papers
Hardware Acceleration of Block-Diffusion LLM for Edge Devices
Wei-Hsing Huang, Kiseok Lee, Ming-Yen Lee +7
Single-stream (batch-one) edge inference cannot amortize weight traffic across requests. Full-attention diffusion LLMs recompute the entire sequence at every step; native block dif…
Hidden APIs in Language Models: Discovering Reusable Causal Interfaces from Forked Futures
SiYuan Ma, Yiqin Luo, Zhangji +8
Identical language-model answers can arise from hidden states that support different future computations, so current-answer probes do not establish a reusable internal interface. W…
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…
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…
3DGauCIM: Accelerating Static/Dynamic 3D Gaussian Splatting via Digital CIM for High Frame Rate Real-Time Edge Rendering
Wei-Hsing Huang, Cheng-Jhih Shih, Jian-Wei Su +10
Dynamic 3D Gaussian splatting (3DGS) extends static 3DGS to render dynamic scenes, enabling AR/VR applications with moving objects. However, implementing dynamic 3DGS on edge devic…
NeuroSim V1.5: Improved Software Backbone for Benchmarking Compute-in-Memory Accelerators with Device and Circuit-level Non-idealities
James Read, Ming-Yen Lee, Wei-Hsing Huang +3
The exponential growth of artificial intelligence (AI) applications has exposed the inefficiency of conventional von Neumann architectures, where frequent data transfers between co…