Mateo Espinosa Zarlenga, Gabriele Dominici, et al.
ICML 2025
Spiking neural networks (SNNs) incorporating biologically plausible neurons hold great promise because of their unique temporal dynamics and energy efficiency. However, SNNs have developed separately from artificial neural networks (ANNs), limiting the impact of deep learning advances for SNNs. Here, we present an alternative perspective of the spiking neuron that incorporates its neural dynamics into a recurrent ANN unit called a spiking neural unit (SNU). SNUs may operate as SNNs, using a step function activation, or as ANNs, using continuous activations. We demonstrate the advantages of SNU dynamics through simulations on multiple tasks and obtain accuracies comparable to, or better than, those of ANNs. The SNU concept enables an efficient implementation with in-memory acceleration for both training and inference. We experimentally demonstrate its efficacy for a music-prediction task in an in-memory-based SNN accelerator prototype using 52,800 phase-change memory devices. Our results open up an avenue for broad adoption of biologically inspired neural dynamics in challenging applications and acceleration with neuromorphic hardware.
Mateo Espinosa Zarlenga, Gabriele Dominici, et al.
ICML 2025
Masaya Kubota, Masaki Kuribayashi, et al.
MobileHCI 2024
Minghong Fang, Zifan Zhang, et al.
CCS 2024
Jianke Yang, Wang Rao, et al.
NeurIPS 2024