5 papers
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…
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…
ATHEENA: A Toolflow for Hardware Early-Exit Network Automation
Benjamin Biggs, Christos-Savvas Bouganis, George A. Constantinides
The continued need for improvements in accuracy, throughput, and efficiency of Deep Neural Networks has resulted in a multitude of methods that make the most of custom architecture…
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…