Neuromorphic on-chip reservoir computing with spiking neural network architectures
This work addresses efficient neuromorphic computing for specific tasks like chaotic system modeling, but it is incremental as it builds on existing reservoir computing and spiking neuron methods.
The paper tackled the problem of optimizing reservoir computing with spiking neural networks for chaotic dynamics and time series forecasting, finding that tailored network architectures via meta-learning improved task-specific performance, with energy analysis on Intel Loihi hardware.
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 Hénon 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.