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This paper presents a novel nearest neighbor search algorithm achieving TPU (Google Tensor Processing Unit) peak performance, outperforming state-of-the-art GPU algorithms with similar level of recall.
On symmetric and asymmetric lshs for inner product search
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When is “nearest neighbor” meaningful?
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Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering
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Roofline: an insightful visual performance model for multicore architectures
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Product quantization for nearest neighbor search
Jegou, H., Douze, M., and Schmid, C. (2010) · 2010
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Randomized selection on the gpu
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A roofline model of energy
Choi, J. W., Bedard, D., Fowler, R., and Vuduc, R. (2013) · 2013
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Matrix computations
Golub, G. H. and Van Loan, C. F. (2013) · 2013
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Machine learning techniques for anomaly detection: an overview
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The inverted multi-index
Babenko, A. and Lempitsky, V. (2014) · 2014
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Scalable nearest neighbor algorithms for high dimensional data
Muja, M. and Lowe, D. G. (2014) · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D. (2014) · 2014
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Hashing for similarity search: A survey
Wang, J., Shen, H. T., Song, J., and Ji, J. (2014) · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015) · 2015
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Practical and optimal lsh for angular distance
Andoni, A., Indyk, P., Laarhoven, T., Razenshteyn, I., and Schmidt, L. (2015) · 2015
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Learning to hash for indexing big data—a survey
Wang, J., Liu, W., Kumar, S., and Chang, S.-F. (2015) · 2015
Statistical analysis of nearest neighbor methods for anomaly detection
Gu, X., Akoglu, L., and Rinaldo, A. (2019) · 2019
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Diskann: Fast accurate billion-point nearest neighbor search on a single node
Jayaram Subramanya, S., Devvrit, F., Simhadri, H. V., Krishnawamy, R., and Kadekodi, R. (2019) · 2019
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Recommending what video to watch next: a multitask ranking system
Zhao, Z., Hong, L., Wei, L., Chen, J., Nath, A., Andrews, S., Kumthekar, A., Sathiamoorthy, M., Yi, X., and Chi, E. (2019) · 2019
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Ann-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms
Aumüller, M., Bernhardsson, E., and Faithfull, A. (2020) · 2020
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Accelerating large-scale inference with anisotropic vector quantization
Guo, R., Sun, P., Lindgren, E., Geng, Q., Simcha, D., Chern, F., and Kumar, S. (2020) · 2020
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In-datacenter performance analysis of a tensor processing unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · 2017
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Exploring gpu performance, power and energy-efficiency bounds with cache-aware roofline modeling
Lopes, A., Pratas, F., Sousa, L., and Ilic, A. (2017) · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J. (2017) · 2017
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Revisiting the inverted indices for billion-scale approximate nearest neighbors
Baranchuk, D., Babenko, A., and Malkov, Y. (2018) · 2018
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JAX: composable transformations of Python+NumPy programs
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q. (2018) · 2018
Cited alongside, same era.
Cer, D., Yang, Y., Kong, S.-y., Hua, N., Limtiaco, N., John, R. S., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., et al. (2018) · 2018
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Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs
Malkov, Y. A. and Yashunin, D. A. (2018) · 2018
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Hm-ann: Efficient billion-point nearest neighbor search on heterogeneous memory
Ren, J., Zhang, M., and Li, D. (2020) · 2020
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Time-based roofline for deep learning performance analysis
Wang, Y., Yang, C., Farrell, S., Zhang, Y., Kurth, T., and Williams, S. (2020) · 2020
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Song: Approximate nearest neighbor search on gpu
Zhao, W., Tan, S., and Li, P. (2020) · 2020
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Improving language models by retrieving from trillions of tokens
Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Driessche, G. v. d., Lespiau, J.-B., Damoc, B., Clark, A., et al. (2021) · 2021
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Spann: Highly-efficient billion-scale approximate nearest neighborhood search
Chen, Q., Zhao, B., Wang, H., Li, M., Liu, C., Li, Z., Yang, M., and Wang, J. (2021) · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q., Sung, Y.-H., Li, Z., and Duerig, T. (2021) · 2021
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Billion-scale similarity search with gpus
Johnson, J., Douze, M., and Jégou, H. (2021) · 2021
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Efficient training of retrieval models using negative cache
Lindgren, E., Reddi, S., Guo, R., and Kumar, S. (2021) · 2021
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The design process for google’s training chips: Tpuv2 and tpuv3
Norrie, T., Patil, N., Yoon, D. H., Kurian, G., Li, S., Laudon, J., Young, C., Jouppi, N., and Patterson, D. (2021) · 2021
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Hierarchical roofline performance analysis for deep learning applications
Yang, C., Wang, Y., Kurth, T., Farrell, S., and Williams, S. (2021) · 2021
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Efficient indexing of billion-scale datasets of deep descriptors
Babenko, A. and Lempitsky, V. (2016) · 2063
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