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

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

Murat Isik, Karn Tiwari, Muhammed Burak Eryilmaz and I. Can Dikmen
IEEE Conference on High Performance Extreme Computing (Online), pp 1-7
23 Sep 2024
DOI: https://doi.org/10.1109/HPEC62836.2024.10938501

Abstract

Autonomous systems Loihi-2 Neuromorphic Computing Neuromorphic engineering Reconfigurable Computing Robot sensing systems Semiconductor device measurement Sensor fusion Spiking neural networks Streams Technological innovation Transistors Energy Efficiency
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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