UNISPIKE: Boosting the Performance of Spiking Neural Networks with Hybrid Training

Spiking neural networks (SNNs) offer an efficient inference paradigm with two desirable properties: addition-only computation and step-by-step inference. Addition-only computation removes expensive multipliers and reduces arithmetic energy, while step-by-step inference enables temporally progressive outputs for faster response and early exiting. However, achieving high accuracy while preserving these properties remains challenging. Several accuracy-oriented SNN operators, such as SEW-addition, temporal attention, and temporal addition, improve representation learning by introducing multi-valued activations or temporal synchronization, thereby violating addition-only computation or step-by-step inference. To improve accuracy under these property-preserving constraints, we propose UniSpike, a hybrid-training framework that initializes an SNN from a quantized ANN and further fine-tunes it with back-propagation-through-time (BPTT). The key design is Unified Clip, a unified ANN-side activation that replaces operators incompatible with spike-based inference, including softmax, layer normalization, and GeLU. Its quantized form is equivalent to the ST-BIF spiking neuron, providing effective ANN-to-SNN initialization for SNN fine-tuning. Built upon Unified Clip, we design UniFormer, a spike-compatible vision transformer tailored for accurate, addition-only, and step-by-step SNN inference. On ImageNet-1K, UniSpike achieves 80.8% accuracy at 4 time-steps, outperforming the SOTA addition-only and step-by-step direct-training method Spike-Driven Transformer V2.