7 citations · 8 across the 12 of their papers we have counts for
5 papers · 1 filter
Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
Tianyi Zhang, Junda Su, Aditya Desai +3
Adapting pre-trained large language models (LLMs) is crucial but challenging due to their enormous size. Parameter-efficient fine-tuning (PEFT) techniques typically employ additive…
LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid
Tianyi Zhang, Anshumali Shrivastava
Large language models (LLMs) have shown immense potential across various domains, but their high memory requirements and inference costs remain critical challenges for deployment.…
IDentity with Locality: An ideal hash for gene sequence search
Aditya Desai, Gaurav Gupta, Tianyi Zhang +1
Gene sequence search is a fundamental operation in computational genomics. Due to the petabyte scale of genome archives, most gene search systems now use hashing-based data structu…
KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization
Tianyi Zhang, Jonah Yi, Zhaozhuo Xu +1
Efficient deployment of Large Language Models (LLMs) requires batching multiple requests together to improve throughput. As the batch size, context length, or model size increases,…
NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention
Tianyi Zhang, Jonah Wonkyu Yi, Bowen Yao +2
Large language model inference on Central Processing Units (CPU) is challenging due to the vast quantities of expensive Multiply-Add (MAD) matrix operations in the attention comput…