# Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs

arXiv: https://arxiv.org/abs/2004.12691 · HTML: https://mlsanity.com/paper/2004.12691/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation](https://mlsanity.com/paper/2110.07742/) (2021)
- [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021)
- [PrivateSNN: Privacy-Preserving Spiking Neural Networks](https://mlsanity.com/paper/2104.03414/) (2021)
- [Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments](https://mlsanity.com/paper/2109.06452/) (2021)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [Hyperseed: Unsupervised Learning with Vector Symbolic Architectures](https://mlsanity.com/paper/2110.08343/) (2021)
- [Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition](https://mlsanity.com/paper/2209.08723/) (2022)
- [Visual Odometry with Neuromorphic Resonator Networks](https://mlsanity.com/paper/2209.02000/) (2022)
- [Deep Unsupervised Learning Using Spike-Timing-Dependent Plasticity](https://mlsanity.com/paper/2307.04054/) (2023)
- [Security-Aware Approximate Spiking Neural Networks](https://mlsanity.com/paper/2301.05264/) (2023)
- [Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing](https://mlsanity.com/paper/2408.15800/) (2024)

## Abstract (arXiv)

Neuromorphic computing applies insights from neuroscience to uncover innovations in computing technology. In the brain, billions of interconnected neurons perform rapid computations at extremely low energy levels by leveraging properties that are foreign to conventional computing systems, such as temporal spiking codes and finely parallelized processing units integrating both memory and computation. Here, we showcase the Pohoiki Springs neuromorphic system, a mesh of 768 interconnected Loihi chips that collectively implement 100 million spiking neurons in silicon. We demonstrate a scalable approximate k-nearest neighbor (k-NN) algorithm for searching large databases that exploits neuromorphic principles. Compared to state-of-the-art conventional CPU-based implementations, we achieve superior latency, index build time, and energy efficiency when evaluated on several standard datasets containing over 1 million high-dimensional patterns. Further, the system supports adding new data points to the indexed database online in O(1) time unlike all but brute force conventional k-NN implementations.