Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
TTFSFormer: A TTFS-based Lossless Conversion of Spiking Transformer
Authors: Lusen Zhao, Zihan Huang, Jianhao Ding, Zhaofei Yu
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on different models demonstrate that our proposed method can achieve high accuracy with significantly lower energy consumption. We evaluate our method on various pre-trained Transformer models, including Vi T and EVA, using the Image Net-1K dataset. Experimental results demonstrate that our approach achieves performance comparable to ANN counterparts. |
| Researcher Affiliation | Academia | 1 Peking University, China. Correspondence to: Zhaofei Yu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Converting ANN into TTFS-based SNN |
| Open Source Code | Yes | The source code of the proposed method is available at https://github.com/ Forest On The Land/TTFSFormer.git. |
| Open Datasets | Yes | In this section, we evaluate our TTFS-based converted SNN methods on the Image Net-1k dataset (Deng et al., 2009) |
| Dataset Splits | No | The paper mentions using the ImageNet-1k dataset but does not explicitly describe any specific training, validation, or test splits, nor does it refer to standard splits with specific percentages or counts. |
| Hardware Specification | No | The paper estimates energy consumption and discusses hardware implementation limitations regarding time precision but does not specify any concrete hardware components (e.g., GPU models, CPU types) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | The whole conversion process is shown in Algorithm 1. We ll discuss some details in this part. A.1. Setting the Constants Since we re using adjustable parameters τ and Tref, we can set the [a, b] such that nearly all outputs lie within the range. More specifically, if the output range is [a, b], we can set bτ = Tref Temit, aτ = Tref Tend, (29) which indicates that τ = δ b a and Tref = Temit + bτ. |