6 papers · 1 filter
An Improved Template for Approximate Computing
Morteza Rezaalipour, Francesco Costa, Marco Biasion +3
Deploying neural networks on edge devices entails a careful balance between the energy required for inference and the accuracy of the resulting classification. One technique for na…
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…
QERA: an Analytical Framework for Quantization Error Reconstruction
Cheng Zhang, Jeffrey T. H. Wong, Can Xiao +2
The growing number of parameters and computational demands of large language models (LLMs) present significant challenges for their efficient deployment. Recently, there is an incr…
Optimised Grouped-Query Attention Mechanism for Transformers
Yuang Chen, Cheng Zhang, Xitong Gao +3
Grouped-query attention (GQA) has been widely adopted in LLMs to mitigate the complexity of multi-head attention (MHA). To transform an MHA to a GQA, neighbour queries in MHA are e…
Unlocking the Global Synergies in Low-Rank Adapters
Zixi Zhang, Cheng Zhang, Xitong Gao +3
Low-rank Adaption (LoRA) has been the de-facto parameter-efficient fine-tuning technique for large language models. We present HeteroLoRA, a light-weight search algorithm that leve…
LQER: Low-Rank Quantization Error Reconstruction for LLMs
Cheng Zhang, Jianyi Cheng, George A. Constantinides +1
Post-training quantization of Large Language Models (LLMs) is challenging. In this work, we introduce Low-rank Quantization Error Reduction (LQER), which combines quantization and…