5 papers
Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification
Hong Huang, Decheng Wu, Qiangqiang Hu +5
The deployment of Large Language Models (LLMs) on resource-constrained edge devices is increasingly hindered by prohibitive memory and computational requirements. While ternary qua…
Extreme Model Compression with Structured Sparsity at Low Precision
Dan Liu, Nikita Dvornik, Xue Liu
Deep neural networks (DNNs) are used in many applications, but their large size and high computational cost make them hard to run on devices with limited resources. Two widely used…
FLASH Viterbi: Fast and Adaptive Viterbi Decoding for Modern Data Systems
Ziheng Deng, Xue Liu, Jiantong Jiang +3
The Viterbi algorithm is a key operator for structured sequence inference in modern data systems, with applications in trajectory analysis, online recommendation, and speech recogn…
Tequila: Trapping-free Ternary Quantization for Large Language Models
Hong Huang, Decheng Wu, Rui Cen +7
Quantization techniques are essential for the deployment of Large Language Models (LLMs) on edge devices. However, prevailing methods often rely on mixed-precision multiplication t…
T-Graphormer: Using Transformers for Spatiotemporal Forecasting
Hao Yuan Bai, Xue Liu
Spatiotemporal data is ubiquitous, and forecasting it has important applications in many domains. However, its complex cross-component dependencies and non-linear temporal dynamics…