6 citations · 12 across the 10 of their papers we have counts for
Showing 2023Show all
2 papers · 1 filter
cs.LG2023★ 1 cited
Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators
Mingi Yoo, Jaeyong Song, Hyeyoon Lee +4
Graph convolutional networks (GCNs) are becoming increasingly popular as they can process a wide variety of data formats that prior deep neural networks cannot easily support. One…
cs.LG2023
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration
Deokki Hong, Kanghyun Choi, Hye Yoon Lee +4
Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-pr…