TETRIS: A Novel FPGA Virtualization Framework for Fine-grained Sharing via Hierarchical Reconfiguration

Authors:

Wenbin Teng, Wenqi Lou, Teng Wang, Lei Gong, Chao Wang, Xuehai Zhou

Published:

ACM Transactions on Reconfigurable Technology and Systems, volume 19, issue 1, pp. 1-32. March 6, 2026.

Abstract:

Field-Programmable Gate Arrays (FPGAs) are increasingly used in cloud platforms to accelerate diverse workloads, thanks to their reconfigurability and high performance. However, in multi-tenant cloud environments, existing FPGA virtualization mechanisms fail to align with dynamic application demands due to their static partitioning methods, leading to significant internal fragmentation. To address these issues, we present Tetris , an FPGA virtualization framework that supports flexible and dynamic reconfigurable resource allocation to improve cloud platform deployment efficiency. Specifically, Tetris deploys nested dynamic reconfigurable regions on FPGAs and adopts a tree structure to manage reconfigurable resources, enabling fine-grained resource reallocation at runtime. Enabled by the proposed fat-tree-based data transmission architecture between reconfigurable regions, Tetris can map dataflow-based applications onto these regions effectively. Additionally, Tetris offers dual-level resource optimization strategies to help system to balance the resource utilization and run-time compilation overhead. We evaluate Tetris with dataflow HLS benchmarks. Experimental results show that Tetris achieves a 1.16 \(\times\) improvement in resource utilization compared to advanced FPGA virtualization frameworks and delivers a 1.3 \(\times\) increase in run-time throughput, incurring less than 30% additional compilation latency. Tetris enables scalable, on-demand cloud FPGA acceleration, improving adaptability across different workloads.

BibTeX:

@article{teng2026,
  title = {{TETRIS: A Novel FPGA Virtualization Framework for Fine-grained Sharing via Hierarchical Reconfiguration}},
  author = {Wenbin Teng and Wenqi Lou and Teng Wang and Lei Gong and Chao Wang and Xuehai Zhou},
  journal = {ACM Transactions on Reconfigurable Technology and Systems},
  volume = {19},
  issue = {1},
  year = 2026,
  month = 3,
  day = 6,
  doi = {10.1145/3779447},
}