AMNES: Accelerating the computation of data correlation using FPGAs
Summary: AMNES offloads correlation computation to an FPGA-based NIC, processing streams at network line-rate (100 Gbps RDMA) for near-data/in-network analytics. Customized FPGA pipeline bypasses CPU/GPU to compute correlations among 64 streams in ~10 ms—an order-of-magnitude speedup over CPU/GPU baselines. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Monica Chiosa (ETH Zurich)
- 2. Thomas B. Preußer (AMD)
- 3. Michaela Blott (AMD)
- 4. Gustavo Alonso (ETH Zurich)
BibTeX Citation
@article{chiosa_vldb23,
title = {{AMNES: Accelerating the computation of data correlation using FPGAs}},
author = {Chiosa, Monica and Preußer, Thomas B. and Blott, Michaela and Alonso, Gustavo},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {13},
pages = {4174--4187},
doi = {10.14778/3625054.3625056},
url = {https://doi.org/10.14778/3625054.3625056},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,716 | nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic Systems | 2024 | VLDB | 5.3776746e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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