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
- 2. Thomas B. Preu
- 3. Michaela Blott
- 4. Gustavo Alonso
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 | 4.4618187e-05 |
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 8,048 | Lowering the Latency of Data Processing Pipelines Through FPGA based Hardware Acceleration | 2020 | VLDB | 4.5977431e-05 |
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