Adaptive Mixed-Precision Computing Speeds Up Nearest Neighbor Search

ANNS-AMP — Mingkai Chen, Cheng Liu, Shengwen Liang, Lei Zhang, Xiaowei Li, Huawei Li · ICT, Chinese Academy of Sciences · June 2026
arXiv 2606.07156 Interactive page

IVF-PQ query: five stages

Click a stage. The paper reports >99.9% of distance arithmetic in stages 1 and 3 goes to non-neighbors.Reported

Where the waste sits

Toy grid of candidates, 3 true neighbors. Switch between fixed 8-bit and per-candidate precision.Illustrative

Dimension slices unlock low precision

Share of sub-spaces qualifying for low precision, from the ablation.Reported: 0% and 87.5%

The runtime predictor

Five inputs, SVR with RBF kernel (evaluated via lookup table), at most 1280 training samples. A linear kernel found no low-precision opportunity.Reported

Bigger gap, fewer bits

Drag to move a cluster away from the top-k cutoff. Mapping is a conceptual sketch, not the paper's fitted model.Illustrative

Accelerator structure

The prediction module reuses the main array and priority queues; there is no second array.Per episode

Bit-plane layout

Top bit of many values stored together, then the next. Fewer bits means fewer planes fetched and fewer bit-serial cycles.Illustrative data bits
>1.18× fewer memory accesses, cluster locating ~3% scheduler gain, table construction ~1.15× with looser accuracy target

Bandwidth ceiling

Throughput flattens near 2000 GB/s at default settings. Curve shape is a schematic.Illustrative

Reported results

14 nm sim7.5 mm²11.5 W1 GHzHBM 1600 GB/s SIFT100M · DEEP100Mrecall@10 ≥ 0.8

What does each gap actually isolate?

Cell lit = ingredient is bundled into that comparison, per the discussion. Pick a baseline or hover a cell.

Gap ratios

Computed from the reported speedups.Derived

The missing experiment

Same accelerator, same bit-serial array, same layout; only the precision policy differs.

References

  1. ANNS-AMP (Chen et al., 2026)
  2. Mixed Precision Training — Micikevicius et al., 2018
  3. HAQ — Wang et al., 2019
  4. HAWQ — Dong et al., 2019
  5. Stripes — Judd et al., 2016
  6. Bit Fusion — Sharma et al., 2018
  7. BISMO — Umuroglu et al., 2018
  8. Product Quantization — Jégou, Douze, Schmid, 2011
  9. Billion-scale similarity search with GPUs — Johnson et al., 2017
  10. Anisotropic Vector Quantization — Guo et al., 2020
  11. FANNS — Jiang et al., 2023
  12. ANSMET — Li et al., 2025
  13. ANNA — Lee et al., 2022
  14. JUNO — Liu et al., 2024
  15. RaBitQ — Gao, Long, 2024
  16. ADSampling — Gao, Long, 2023
  17. Quicker ADC / Fast Scan — André et al., 2019