GenMap: A Genetic Algorithmic Approach for Optimizing Spatial Mapping of Coarse-Grained Reconfigurable Architectures

Author:

Kojima TakuyaORCID,Doan Nguyen Anh VuORCID,Amano Hideharu

Funder

JSPS KAKENHI

VLSI Design and Education Center (VDEC), The University of Tokyo, in collaboration with Synopsys, Inc., and Cadence Design Systems, Inc.

JST CREST

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Software

Cited by 28 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. RoDMap: A Reserve-on-Demand Mapper for Spatially-Configured Coarse-Grained Reconfigurable Arrays;Proceedings of the 53rd International Conference on Parallel Processing;2024-08-12

2. Exploration of Trade-offs Between General-Purpose and Specialized Processing Elements in HPC-Oriented CGRA;2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2024-05-27

3. TransMap: An Efficient CGRA Mapping Framework via Transformer and Deep Reinforcement Learning;2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2024-05-27

4. Mapping Enumeration for Multi-Context CGRAs Using Zero-Suppressed Binary Decision Diagrams;2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM);2024-05-05

5. E2EMap: End-to-End Reinforcement Learning for CGRA Compilation via Reverse Mapping;2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA);2024-03-02

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