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arXiv · 2408.16096

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

Murat Isik·Karn Tiwari·Muhammed Burak Eryilmaz·I. Can Dikmen
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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.

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Murat Isik, Karn Tiwari, Muhammed Burak Eryilmaz, I. Can Dikmen. 2024-08-28. Accelerating Sensor Fusion in Neuromorphic Computing: A Case Study on Loihi-2. https://arxiv.org/abs/2408.16096

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