Reproduce “Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Zecheng Hao, Qichao Ma, Kang Chen, Yi Zhang, Zhaofei Yu, Tiejun Huang
The authors introduce a parallel conversion method that maps artificial neural network activations to ordered spiking outputs, allowing time-step calculations together rather than simulating neuron updates serially. Calibration adjusts activation distributions without fine-tuning model weights. On ImageNet-1k, calibrated ResNet-34 reaches 72.90% accuracy at four steps, versus 74.30% for its source network; this is a step-count, not elapsed-time comparison.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.