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This paper presents a novel nearest neighbor search algorithm achieving TPU(Google Tensor Processing Unit) peak performance, outperformingstate-of-the-art GPU algorithms with similar level of recall. The design of theproposed algorithm is motivated by an accurate accelerator performance modelthat takes into account both the memory and instruction bottlenecks. Ouralgorithm comes with an analytical guarantee of recall in expectation and doesnot require maintaining sophisticated index data structure or tuning, making itsuitable for applications with frequent updates. Our work is available in theopen-source package of Jax and Tensorflow on TPU.

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s