257 citations · 483 across the 39 of their papers we have counts for
41 papers
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…
A3 : an Analytical Low-Rank Approximation Framework for Attention
Jeffrey T. H. Wong, Cheng Zhang, Xinye Cao +5
Large language models have demonstrated remarkable performance; however, their massive parameter counts make deployment highly expensive. Low-rank approximation offers a promising…
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…
Exploring FPGA designs for MX and beyond
Ebby Samson, Naveen Mellempudi, Wayne Luk +1
A number of companies recently worked together to release the new Open Compute Project MX standard for low-precision computation, aimed at efficient neural network implementation.…
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…