PDL Abstract Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination SIGMOD ’20, June 14–19, 2020, Virtual Portland, OR, USA. Conglong Li, Minjia Zhang*, David G. Andersen, Yuxiong He* Carnegie Mellon University *Microsoft AI and Research http://www.pdl.cmu.edu/ In applications ranging from image search to recommendation systems, the problem of identifying a set of “similar” real-valued vectors to a query vector plays a critical role. However, retrieving these vectors and computing the corresponding similarity scores from a large database is computationally challenging. Approximate nearest neighbor (ANN) search relaxes the guarantee of exactness for efficiency by vector compression and/or by only searching a subset of database vectors for each query. Searching a larger subset increases both accuracy and latency. State-of-the-art ANN approaches use fixed configurations that apply the same termination condition (the size of subset to search) for all queries, which leads to undesirably high latency when trying to achieve the last few percents of accuracy. We find that due to the index structures and the vector distributions, the number of database vectors that must be searched to find the ground-truth nearest neighbor varies widely among queries. Critically, we further identify that the intermediate search result after a certain amount of search is an important runtime feature that indicates how much more search should be performed. To achieve a better tradeoff between latency and accuracy, we propose a novel approach that adaptively determines search termination conditions for individual queries. To do so, we build and train gradient boosting decision tree models to learn and predict when to stop searching for a certain query. These models enable us to achieve the same accuracy with less total amount of search compared to the fixed configurations. We apply the learned adaptive early termination to state-of-the-art ANN approaches, and evaluate the end-to-end performance on three million to billion-scale datasets. Compared with fixed configurations, our approach consistently improves the average end-to-end latency by up to 7.1 times faster under the same high accuracy targets. Our approach is open source at github.com/efficient/faisslearned- termination. FULL TR: pdf Parallel Data Laboratory

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PDL Abstract

Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination

SIGMOD ’20, June 14–19, 2020, Virtual Portland, OR, USA.

Conglong Li, Minjia Zhang*, David G. Andersen, Yuxiong He*

Carnegie Mellon University
*Microsoft AI and Research

http://www.pdl.cmu.edu/

In applications ranging from image search to recommendation systems, the problem of identifying a set of “similar” real-valued vectors to a query vector plays a critical role. However, retrieving these vectors and computing the corresponding similarity scores from a large database is computationally challenging. Approximate nearest neighbor (ANN) search relaxes the guarantee of exactness for efficiency by vector compression and/or by only searching a subset of database vectors for each query. Searching a larger subset increases both accuracy and latency. State-of-the-art ANN approaches use fixed configurations that apply the same termination condition (the size of subset to search) for all queries, which leads to undesirably high latency when trying to achieve the last few percents of accuracy. We find that due to the index structures and the vector distributions, the number of database vectors that must be searched to find the ground-truth nearest neighbor varies widely among queries. Critically, we further identify that the intermediate search result after a certain amount of search is an important runtime feature that indicates how much more search should be performed.

To achieve a better tradeoff between latency and accuracy, we propose a novel approach that adaptively determines search termination conditions for individual queries. To do so, we build and train gradient boosting decision tree models to learn and predict when to stop searching for a certain query. These models enable us to achieve the same accuracy with less total amount of search compared to the fixed configurations. We apply the learned adaptive early termination to state-of-the-art ANN approaches, and evaluate the end-to-end performance on three million to billion-scale datasets. Compared with fixed configurations, our approach consistently improves the average end-to-end latency by up to 7.1 times faster under the same high accuracy targets. Our approach is open source at github.com/efficient/faisslearned- termination.

FULL TR: pdf