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

TL;DR

This paper demonstrates that Intel's Loihi-2 neuromorphic chip significantly accelerates sensor fusion tasks in robotics and autonomous systems, offering over 100 times the energy efficiency and faster processing speeds compared to traditional CPUs and GPUs.

Contribution

The study presents a novel application of Loihi-2 for sensor fusion, showcasing its superior speed and energy efficiency in processing multiple complex datasets.

Findings

Loihi-2 outperforms CPUs and GPUs in energy efficiency by over 100x and 30x respectively.

Loihi-2 achieves faster processing speeds on diverse sensor datasets.

Implementation challenges and architectural benefits of Loihi-2 are discussed.

Abstract

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.