Data-Flow Graph Mapping Optimization for CGRA With Deep Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2018.2878183
Abstract: Coarse-grained reconfigurable architectures (CGRAs) have drawn increasing attention due to their flexibility and energy efficiency. Data flow graphs (DFGs) are often mapped onto CGRAs for acceleration. The problem of DFG mapping is challenging due to… read more here.
Keywords: flow graph; cgra; reinforcement learning; deep reinforcement ... See more keywords
Specializing CGRAs for Light-Weight Convolutional Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2021.3123178
Abstract: Deep neural network (DNN) processing units, or DPUs, are one of the most energy-efficient platforms for DNN applications. However, designing new DPUs for every DNN model is very costly and time consuming. In this article,… read more here.
Keywords: specializing cgras; cgra; light weight; cgras light ... See more keywords
HiMap: Fast and Scalable High-Quality Mapping on CGRA via Hierarchical Abstraction
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2021.3132551
Abstract: Coarse-grained reconfigurable array (CGRA) has emerged as a promising hardware accelerator due to the excellent balance between reconfigurability, performance, and energy efficiency. The performance of a CGRA strongly depends on the existence of a high-quality… read more here.
Keywords: italic himap; cgra; himap italic; tex math ... See more keywords