# Hyperdimensional Computing with Spiking-Phasor Neurons

## TL;DR

This paper explores implementing Vector Symbolic Architectures using spiking neurons to leverage neuromorphic hardware for efficient cognitive computing tasks.

## Contribution

It introduces a novel approach to run VSA algorithms on spiking neuron substrates suitable for neuromorphic hardware.

## Key findings

- Demonstrates feasibility of VSA on spiking neurons
- Achieves efficient spatial reasoning and symbol binding
- Reduces energy consumption compared to traditional methods

## Abstract

Vector Symbolic Architectures (VSAs) are a powerful framework for representing compositional reasoning. They lend themselves to neural-network implementations, allowing us to create neural networks that can perform cognitive functions, like spatial reasoning, arithmetic, symbol binding, and logic. But the vectors involved can be quite large, hence the alternative label Hyperdimensional (HD) computing. Advances in neuromorphic hardware hold the promise of reducing the running time and energy footprint of neural networks by orders of magnitude. In this paper, we extend some pioneering work to run VSA algorithms on a substrate of spiking neurons that could be run efficiently on neuromorphic hardware.