6 citations · 9 across the 2 of their papers we have counts for
2 papers
cs.AR2025★ 3 cited
MetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep Learning Acceleration
Zhiqiang Que, Jose G. F. Coutinho, Ce Guo +2
This paper presents a unified framework for codifying and automating optimization strategies to efficiently deploy deep neural networks (DNNs) on resource-constrained hardware, suc…
cs.LG2023★ 6 cited
MetaML: Automating Customizable Cross-Stage Design-Flow for Deep Learning Acceleration
Zhiqiang Que, Shuo Liu, Markus Rognlien +3
This paper introduces a novel optimization framework for deep neural network (DNN) hardware accelerators, enabling the rapid development of customized and automated design flows. M…