Neuromorphic on-chip reservoir computing with spiking neural network architectures
<|reference_start|>Neuromorphic on-chip reservoir computing with spiking neural network architectures: Reservoir computing is a promising approach for harnessing the computational power of recurrent neural networks while dramatically simplifying training. This paper investigates the application of integrate-and-fire neurons within reservoir computing frameworks for two distinct tasks: capturing chaotic dynamics of the H\'enon map and forecasting the Mackey-Glass time series. Integrate-and-fire neurons can be implemented in low-power neuromorphic architectures such as Intel Loihi. We explore the impact of network topologies created through random interactions on the reservoir's performance. Our study reveals task-specific variations in network effectiveness, highlighting the importance of tailored architectures for distinct computational tasks. To identify optimal network configurations, we employ a meta-learning approach combined with simulated annealing. This method efficiently explores the space of possible network structures, identifying architectures that excel in different scenarios. The resulting networks demonstrate a range of behaviors, showcasing how inherent architectural features influence task-specific capabilities. We study the reservoir computing performance using a custom integrate-and-fire code, Intel's Lava neuromorphic computing software framework, and via an on-chip implementation in Loihi. We conclude with an analysis of the energy performance of the Loihi architecture.<|reference_end|>
@article{karki2024neuromorphic,
    title={Neuromorphic on-chip reservoir computing with spiking neural network
  architectures},
    author={Samip Karki, Diego Chavez Arana, Andrew Sornborger, Francesco
  Caravelli},
    journal={arXiv preprint arXiv:2407.20547},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20547},
    primaryClass={cs.NE cs.ET cs.LG}
}