A Performance Modelling-Driven Approach to Hardware Resource Scaling
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48803-0_15
Reference18 articles.
1. PolyBench/C. https://web.cse.ohio-state.edu/~pouchet.2/software/polybench/
2. Agrawal, R., et al.: FAB: An FPGA-based Accelerator for Bootstrappable Fully Homomorphic Encryption (2022). arXiv:2207.11872
3. Bobda, C., et al.: The future of FPGA acceleration in datacenters and the cloud. ACM Trans. Reconfigurable Technol. Syst. 15(3), 1–42 (2022)
4. Cavalcante, M., et al.: Ara: a 1-GHz+ scalable and energy-efficient RISC-V vector processor with multiprecision floating-point support in 22-nm FD-SOI. IEEE Trans. Very Large Scale Integr. Syst. 28(2), 530–543 (2020)
5. Chen, X., et al.: ReGraph: Scaling Graph Processing on HBM-enabled FPGAs with Heterogeneous Pipelines. Technical report, arXiv:2203.02676, arXiv (2022)
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