ICML 2025

Temporal Misalignment in ANN-SNN Conversion and
its Mitigation via Probabilistic Spiking Neurons
Velibor Bojkovi´c * 1 Xiaofeng Wu * 2 Bin Gu 3
Abstract
Spiking Neural Networks (SNNs) offer a more
energy-efficient alternative to Artificial Neural
Networks (ANNs) by mimicking biological neu-
ral principles, establishing them as a promising ap-
proach to mitigate the increasing energy demands
of large-scale neural models. However, fully har-
nessing the capabilities of SNNs remains chal-
lenging due to their discrete signal processing and
temporal dynamics. ANN-SNN conversion has
emerged as a practical approach, enabling SNNs
to achieve competitive performance on complex
machine learning tasks. In this work, we identify a
phenomenon in the ANN-SNN conversion frame-
work, termed temporal misalignment, in which
random spike rearrangement across SNN layers
leads to performance improvements. Based on
this observation, we introduce biologically plau-
sible two-phase probabilistic (TPP) spiking neu-
rons, further enhancing the conversion process.
We demonstrate the advantages of our proposed
method both theoretically and empirically through
comprehensive experiments on CIFAR-10/100,
CIFAR10-DVS, and ImageNet across a variety
of architectures, achieving state-of-the-art results.
Codes are available at https://github.
com/shizukanaskytree/TPP-SNN.
1. Introduction
Spiking neural networks (SNNs), often referred to as the
third generation of neural networks (Maass, 1997), closely
mimic biological neuronal communication through dis-
crete spikes (McCulloch & Pitts, 1943; Hodgkin & Hux-
*Equal contribution
1Department of Machine Learning, Mo-
hamed bin Zayed University of Artificial Intelligence, Abu Dhabi,
United Arab Emirates 2Faculty of Data Science, City University
of Macau, Macau, China 3School of Artificial Intelligence, Jilin
University, China. Correspondence to: Velibor Bojkovic <veli-
bor.bojkovic@mbzuai.ac.ae>.
Proceedings of the 42 nd International Conference on Machine...