IEEE Transactions on Very Large Scale Integration (VLSI) Systems· 2025Q1
FANNS: An FPGA-Based Approximate Nearest-Neighbor Search Accelerator
- 6citations
- Q1SCImago
- 2025year
Short summary
FANNS, an FPGA-based accelerator for approximate nearest-neighbor search (ANNS), achieves up to 184x better energy efficiency than CPUs and GPUs by eliminating redundant computations and reusing on-chip data.
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Key points
- FANNS is an FPGA-based accelerator for high-dimensional approximate nearest-neighbor search (ANNS).
- It eliminates redundant computations and reuses on-chip data to improve efficiency.
- FANNS achieves up to 184.1x better energy efficiency compared to CPUs.
- It outperforms GPUs by 33.0x and state-of-the-art ANNS architectures (DF-GAS, Vstore) by 2.9x and 2.5x, respectively.
AI-generated from the title and abstract; the full text is not read.
Abstract
Approximate nearest-neighbor search (ANNS) based on high-dimensional vectors has been extensively utilized in data science and neural networks. However, deploying ANNS in production systems requires minimal redundant computation, high recall rates, and low on-chip memory usage, which existing hardware accelerators fail to offer. We propose FANNS, a solution for ANNS based on high-dimensional vectors that can eliminate redundant computations and reuse on-chip data. Extensive evaluations show that FANNS achieves an average of$184.1\times $,$33.0\times $,$2.9\times $, and$2.5\times $better energy efficiency than CPUs, GPUs, and two state-of-the-art ANNS architectures, i.e., DF-GAS and Vstore, respectively.
The authors' abstract, as published at the source. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2025 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science