DRIPS: Dynamic Rebalancing of Pipelined Streaming Applications on CGRAs

Author:

Tan Cheng1,Agostini Nicolas Bohm1,Geng Tong1,Xie Chenhao,Li Jiajia2,Li Ang1,Barker Kevin J.1,Tumeo Antonino1

Affiliation:

1. Pacific Northwest National Laboratory

2. William & Mary

Publisher

IEEE

Cited by 7 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. 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

2. FDRA: A Framework for a Dynamically Reconfigurable Accelerator Supporting Multi-Level Parallelism;ACM Transactions on Reconfigurable Technology and Systems;2024-01-27

3. Improving Utilization of Dataflow Unit for Multi-Batch Processing.;ACM Transactions on Architecture and Code Optimization;2023-12-18

4. Towards Efficient Control Flow Handling in Spatial Architecture via Architecting the Control Flow Plane;56th Annual IEEE/ACM International Symposium on Microarchitecture;2023-10-28

5. ML-CGRA: An Integrated Compilation Framework to Enable Efficient Machine Learning Acceleration on CGRAs;2023 60th ACM/IEEE Design Automation Conference (DAC);2023-07-09

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