STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking

• URL: http://arxiv.org/abs/2505.11151v1
• Date: Fri, 16 May 2025 11:50:14 GMT
• Title: STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking
• Authors: Sicheng Shen, Dongcheng Zhao, Linghao Feng, Zeyang Yue, Jindong Li, Tenglong Li, Guobin Shen, Yi Zeng,
• Abstract summary: Spiking Transformers have emerged as promising architectures for combining the efficiency of spiking neural networks with the representational power of self-attention.<n>We present a unified benchmark framework for Spiking Transformers that supports a wide range of tasks, including classification, segmentation, and detection.<n>We propose a unified analytical model for energy estimation, accounting for spike sparsity, bitwidth, and memory access, and show that quantized ANNs may offer comparable or better energy efficiency.
• Score: 5.660272448194108
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking Transformers have recently emerged as promising architectures for combining the efficiency of spiking neural networks with the representational power of self-attention. However, the lack of standardized implementations, evaluation pipelines, and consistent design choices has hindered fair comparison and principled analysis. In this paper, we introduce \textbf{STEP}, a unified benchmark framework for Spiking Transformers that supports a wide range of tasks, including classification, segmentation, and detection across static, event-based, and sequential datasets. STEP provides modular support for diverse components such as spiking neurons, input encodings, surrogate gradients, and multiple backends (e.g., SpikingJelly, BrainCog). Using STEP, we reproduce and evaluate several representative models, and conduct systematic ablation studies on attention design, neuron types, encoding schemes, and temporal modeling capabilities. We also propose a unified analytical model for energy estimation, accounting for spike sparsity, bitwidth, and memory access, and show that quantized ANNs may offer comparable or better energy efficiency. Our results suggest that current Spiking Transformers rely heavily on convolutional frontends and lack strong temporal modeling, underscoring the need for spike-native architectural innovations. The full code is available at: https://github.com/Fancyssc/STEP

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