1 citations · 1 across the 6 of their papers we have counts for
6 papers
Controlled LLM Training on Spectral Sphere
Tian Xie, Haoming Luo, Haoyu Tang +9
Scaling large models requires optimization strategies that ensure rapid convergence grounded in stability. Maximal Update Parametrization (P) provides a theoretical s…
LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs
Zifan He, Shengyu Ye, Rui Ma +2
The rapid development of large language models (LLM) has greatly enhanced everyday applications. While many FPGA-based accelerators, with flexibility for fine-grained data control,…
LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator
Guoyu Li, Shengyu Ye, Chunyun Chen +6
The emergence of neural network capabilities invariably leads to a significant surge in computational demands due to expanding model sizes and increased computational complexity. T…
BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration
Yuzong Chen, Ahmed F. AbouElhamayed, Xilai Dai +4
Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders thei…
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models
Yifei Liu, Jicheng Wen, Yang Wang +5
Scaling model size significantly challenges the deployment and inference of Large Language Models (LLMs). Due to the redundancy in LLM weights, recent research has focused on pushi…
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup
Xiaohu Tang, Yang Wang, Ting Cao +5
On-device Deep Neural Network (DNN) inference consumes significant computing resources and development efforts. To alleviate that, we propose LUT-NN, the first system to empower in…