Accelerating Sensor Fusion in Neuromorphic Computing: A Case Study on Loihi-2
<|reference_start|>Accelerating Sensor Fusion in Neuromorphic Computing: A Case Study on Loihi-2: 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.<|reference_end|>
@article{isik2024accelerating,
    title={Accelerating Sensor Fusion in Neuromorphic Computing: A Case Study on
  Loihi-2},
    author={Murat Isik, Karn Tiwari, Muhammed Burak Eryilmaz, I. Can Dikmen},
    journal={arXiv preprint arXiv:2408.16096},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16096},
    primaryClass={cs.AR}
}