# Accelerating Sensor Fusion in Neuromorphic Computing: A Case Study on Loihi-2

arXiv: https://arxiv.org/abs/2408.16096 · HTML: https://mlsanity.com/paper/2408.16096/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2024.

## Built on by (most cited first, 3 of 3)

- [Battery State of Health Estimation Using LLM Framework](https://mlsanity.com/paper/2501.18123/) (2025)
- [Sleep-Based Homeostatic Regularization for Stabilizing Spike-Timing-Dependent Plasticity in Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2601.08447/) (2026)
- [Exploring napping paradigm for Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2609.13927/) (2026)

## Abstract (arXiv)

In our study, we utilized Intel's Loihi-2 neuromorphic chip to enhance sensor fusion in fields like robotics and autonomous systems, focusing on datasets such as AIODrive, Oxford Radar RobotCar, D-Behavior (D-Set), nuScenes by Motional, and Comma2k19. Our research demonstrated that Loihi-2, using spiking neural networks, significantly outperformed traditional computing methods in speed and energy efficiency. Compared to conventional CPUs and GPUs, Loihi-2 showed remarkable energy efficiency, being over 100 times more efficient than a CPU and nearly 30 times more than a GPU. Additionally, our Loihi-2 implementation achieved faster processing speeds on various datasets, marking a substantial advancement over existing state-of-the-art implementations. This paper also discusses the specific challenges encountered during the implementation and optimization processes, providing insights into the architectural innovations of Loihi-2 that contribute to its superior performance.