TP-Spikformer: Token Pruned Spiking Transformer
Authors: Wenjie Wei
Xiaolong Zhou
Malu Zhang
Ammar Belatreche
Qian Sun
Yimeng Shan
Dehao Zhang
Zijian Zhou
Zeyu Ma
Yang Yang
Haizhou Li
Venue: ICLR 2026 Poster (2026)
Keywords: Spiking Neural Networks
Efficient Spiking Neural Network
Pruning Spiking Neural Network
Primary area: applications to neuroscience & cognitive science
TL;DR: We propose a simple yet effective token pruning method for spiking transformers, termed TP-Spikformer, that reduces storage and computational overhead while maintaining competitive performance. TP-Spikformer performs well in a training-free manner.

Abstract: Spiking neural networks (SNNs) offer an energy-efficient alternative to traditional neural networks due to their event-driven computing paradigm. However, recent advancements in spiking transformers have focused on improving accuracy with large-scale architectures, which require significant computational resources and limit deployment on resource-constrained devices. In this paper, we propose a simple yet effective token pruning method for spiking transformers, termed TP-Spikformer, that reduces storage and computational overhead while maintaining competitive performance. Specifically, we first introduce a heuristic spatiotemporal information-retaining criterion that comprehensively evaluates tokens' importance, assigning higher scores to informative tokens for retention and lower scores to uninformative ones for pruning. Based on this criterion, we propose an information-retaining token pruning framework that employs a block-level early stopping strategy for uninformative tokens, instead of removing them outright. This also helps preserve more information during token pruning. We demonstrate the effectiveness, efficiency and scalability of TP-Spikformer through extensive experiments across diverse architectures, including Spikformer, QKFormer and Spike-driven Transformer V1 and V3, and a range of tasks such as image classification, object detection, semantic segmentation and event-based object tracking. Particularly, TP-Spikformer performs well in a training-free manner. These results reveal its potential as an efficient and practical solution for deploying SNNs in real-world applications with limited computational resources.

Published as a conference paper at ICLR 2026
TP-SPIKFORMER: TOKEN PRUNED SPIKING TRANSFORMER
Wenjie Wei', Xiaolong Zhou', Malu Zhang' 1:; Ammar Belatreche", Qian Sun', Yimeng Shan', Dehao Zhang', Zijian Zhou', Zeyu Ma', Yang Yang', Haizhou Li !University of Electronic Science and Technology of China, "Shenzhen Loop Area Institute, Northumbria University, *The Chinese University of Hong Kong, Shenzhen
ABSTRACT
Spiking neural networks (SNNs) offer an energy-efficient alternative to traditional neural networks due their event-driven computing paradigm. However, recent advancements in spiking transformers have focused on improving accuracy with large-scale architectures, which require significant computational resources and limit deployment on snoource-constriigg devices. In this paper. we propose a simple yet effective token pruning method for spiking transformers, termed TP- Spikformer, that reduces storage and computational overhead while maintaining competitive performance. Specifically, we first introduce a heuristic spatiotemporal information-rtainning criterion that comprehensively evaluates tokens' importance, assigning higher scores to informative tokens for retention and lower scores to uninformative ones for pruning. Based on this criterion, propose information- retaining token pruning framework that employs a block-level early stopping strat- egy for uninformative tokens, instead of removing them outright. This also helps preserve more information during token pruning. We demonstrate the effective- ness, efficiency and scalability of TP-Spikformer through extensive experiments across diverse architectures, including Spikformer, QKFormer and Spike-driven Transformer V1 and V3, and a range of tasks such as image classification, object detection, semantic segmentation and event beset object tracking. Particularly, TP-Spikformer performs well in a training- free manner. These results reveal its potential as efficient and practical solution for deploying SNNs in real-world applications with limited computational resources.
INTRODUCTION
Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient solution for next- generation machine intelligence Gerstner & Kistler (2002) Izhikevich (2003). In SNNs, the discrete binary spike serves as the fundamental information carrier and is conveyed event-drivenly. This unique computing paradigm allows only a subset of neurons be activated and engage synaptic accumulation operations, achieving significant computational efficiency Pfeiffer & Pfeil (2018); Roy al. (2019); Li et al. (2024). In addition, the sparse event-driven nature of SNNs has spurred the development of neuromorphic hardware, such as TrueNorth /Akopyan et al. (2015) and Loihi Davies] jet al. (2018), further harnessing their potential for energy efficiency. Despite the significant efficiency advantages, the limited performance of SNNs presents challenges to their widespread applications.
anteligence(GGersner
Building on the success of Transformer models across various fields Devlin et al. (2019); Doso vitskiy al. (2020), researchers have integrated them with SNNs, such as Spikformer Zhou et al. QKFormeiZhaal et al. (2024), Spike-driven cunsformer(SDTT-vV Yao et al. (023a)). V2 Yao et al. and V3 Yao et al. (025), leading to significant performance improvements benchmarks. However, these gains come at the expense of a large number of model parameters and high computational complexity. For example, the recently introduced SDT- V3 Yao al. (2025) achieves 86.2% accuracy on ImageNet. Yet. this model contains 173 million parameters, requires 1384MB of memory, and performs 28.4 billion synaptic operations per second during inference.
large and complex
"Corresponding author; maluzhang@@ustt.eduu..

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Published as a conference paper at ICLR 2026
Block
Block2
Block3
Block4
Block5
Block6
Block?
Block8
T=1
T-2
Prune Ratio
0.0
1/ = 0.27 = 0.27 r4 = 0.27 1g = 0.49 1% = 0.49
0.59 Ya = 0.59
Figure 1: Visualization of token pruning across time step and block with method. Experiments are conducted on SDT-VI-8-511 and white areas are pruned tokens.
These present significant challenges for deploying Transformer-based SNNs resource-lmitted scenarios Qiu et al. ) Zhan et al. (2025).
Researchers have made significant efforts to compress large-scale spiking transformers, including techniques of quantization Cao et al. (2025); Wei et al. (2024a), network architecture search Wang et al. (2024b); Che et al. (2024): Zhang al. (2025), and pruning (Zhgge et al. (2024); Zhou et al. (2024c). Among these, token pruning dynamically reduces the number of tokens processed in each block during inference, enhancing both storage and computational efficiency. The underlying principle behind it is that, in visual tasks, the final prediction typically relies on only subset of the tokens. This allows us to selectively remove certain tokens, accelerating inference while maintaining competitive accuracy Rao et al. [2021); Yin et al. (2022). Therefore, token pruning poses a promising solution for the efficient deployment of spiking transformers, particularly edge scenarios. However, current token pruning methods in SNNs suffer from two major limitations Zhuge et al. (2024); Liu ct al. 2024): Kang et al. (2024). First, most approaches modify the original structure when applied to spiking transformers, such introducing tokens, adding trainable modules, or altering network connections. Second, these methods typically require retraining the model, resulting in large training costs. These issues raise the application costs and reduce their generalizability.
In this paper, we propose a simple yet effective token pruning approach for spiking transformer (TP-Spikformer)), aiming to compress its storage and accelerate its computation while maintain- ing competitive performance. We first propose a heuristic spatiotemporal information-retainin token pruning criterion (IRToP), where informative tokens assigned higher scores retain and uninformative tokens are given lower scores to prune. Based on this criterion, we design an infornation-retaiiing token pruning architecture (IR-Are), which achieves compression and accelera- tion by applying a block-level early stopping strategy for uninformative tokens, rather than direct dropping. This also helps retain more information during token pruning. The token pruning results of TP. TP-Spikformer are depicted in Figure and our main contributions are summarized as follows:
We propose a heuristic spatiotemporal information cettmatio-rettiiingg criterion for token pruning, termed IRToP. Spatially, IRToP recognizes tokens that differ significantly from neighbors as more distinctive. Temporally, IRToP identifies tokens with greater variation across adjacent time steps carriers of richer temporal information. By integrating both aspects, IRToP effectively identifies informative tokens and assigns them higher retention priority.
We propose an intermattionretentiion token pruning architecture, named IR-Are, where infor- mative tokens undergo complete forward computation but uninformative tokens implement block-level early stopping strategy, reducing storage and computation overhead effectively, IR-Are makes TP-Spikformer exhibit high versatility and requires no training from scratch, attaining competitive performance even under zero fine-tuning conditions.
We select variety of architectures for our experiments, including the feature-map invariant Spikformer and SDT-V1, the feature pyramid-bar based QKFormer, and the advanced SDT-V3, Additionally, we assess TP- Spikffrfrrerr on multiple tasks such classification, segmentation, detection, and tracking. Through validation across multiple architectures and tasks, we demonstrate the effectiveness, efficiency, and scalability of our method.
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2 RELATED WORK
Spikformer pioneers self-attention and direct Transformer training in SNNs Zhou et al.; which eliminates float multiplication in attention via spike- Q, K, and V. Spikformer V2 explorss masked image modeling in spiking transformers, achieving 81.1% % accuracy ImageNet with just time step Zhou et al. (2024b). SpikingResformer proposes a dual-spike self-attention and combines it with ResNet-based architecture, improving performance with reduced parameters Shi et al. (2024). QKFormer uses spike-based Q and K for attention computation and introduces spiking patch embeddings with deformable shortcuts, achieving milestone results on multiple datasets Zhou et al. (2024a). In the SNN community, the series of Spike-driven Transformer has gained notable attention. SDT-V1 pioneers spike-driven computation in spiking transformers, converting spike-related matrix multiplications to efficient addition operations Yao et al. (2023a). SDT-V2 extends SDT-VI into a meta architecture, exploring structure design, spike-driven attention, and skip connection to enhance performance Yao et al.) SDT-V3 optimizes spiking neuron firing patterns and designs an efficient Transformer Yao et al. (2025). Despite substantial accuracy improvements, SNNs' inherent energy efficiency is undermined, limiting their deployment in resource cesource-limited scenarios.
Spiking transformer.
Token pruning in spiking transformer. SparseSpikformer proposes a hybrid pruning framework operating at both weight and token levels, removing unimportant background tokens based neurons' spike firing rates Liu et al. (024). However, it has two limitations: it relies on firing rate for token importance without leveraging SNNs' temporal characteristics. and its validation is limited architecture and small-scale datasets, leaving its scalability to other architectures and benchmarks unexplored. AT-SNN adopts an adaptive computation time (ACT) mechanism to mask unimportant tokens using Halting Scores during training, followed by similarity- token merging strategy to reduce computational overhead Kang al. (2024). However, ACT introduces additional parameters requiring retraining, and AT-SNN's validation is also limited to a single architecture and simple datasets. Recently, STATA introduces an anchor token for token pruning with dual temporal and inter-layer alignment mechanisms, becoming the first token pruning method in spiking transformer validated on ImageNet Zhuge et al. a (2024). However, it requires a complete retraining process, and the additional loss terms increase training overhead compared to uncompressed counterparts.
single
3 PRELIMINARY
Spiking neuron model. Spiking neurons mimic the information transmission and processing of biological neurons. Due to the high computational complexity of biological neurons, researchers simplify spiking neurons into differential equations for computer simulation. The neural behavior of spiking neurons typically includes three mechanisms: membrane potential integration, spike generation, and reset Wu et al. (2018): Nefiii et al. (2019):ZZhag et al. (2021b). Below, we describe these hehaviors using the widely adopted Leaky Integrate-and-Fire (LIIF) model, which can be described as follows:
a' [t] = u u't - 1] + f(w", s (t))
(Voltage integration),
(1)
s [1] Heavside
0), [t] (1 s s((t))
(Spike generation),
(2)
hard reset,
u [4] ( 0s [4] soft reset,
(Reset mechanism),
where 7 is the constant leaky factor, t the time step, w' is the weight matrix of layer E, and f(-) is the convolution or linear operation followed by batch normalization (BN). As described above, neurons integrate inputs and emit a spike s E {0. 1} when the membrane potential û exceeds the threshold 0. After spike emission, the reset mechanism is invoked update the membrane potential.
Spiking transformer. The spiking transformer architecture typically comprises four components: input embedding, spiking self-attention (SSA), multi-layer perceptron (MLP), and a classification head (CH). Given a 2D image sequence I, the input embedding module linearly projects it into D-dimensional spiking features vector and partitions it into either a 2D grid of H x W spiking patches or N flattened spiking patches. After adding positional encoding, the initial feature x passes through I. transformer blocks, each containing SSA and MT.P modules. Finally, the features X are
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aggregated via global average pooling (GAP) and processed by the CH to generate predictions:
X InpucEmbedding (T),
X E RTXxwDD
(4)
X SSA(X x
X E R _- L...,
(5)
X = MLP(X() + +X
X E RIxHxwxD, 1-=
(6)
Y CH(GAP(X")),
(7)
Notably, SSA provides an efficient approach to model the local-global information of images using spike-based queries (q), keys (k), and values (v) without employing softmax, described as,
SSA(X _ )vs), X = (X)x),
(8)
x == SN x fE,q, v).
(9)
This design combines the efficiency of SNNs with the modeling capabilities of Transformers, enabling effective processing of information with reduced computational overhead Yao et al. (2023a)
4
METHOD
In this section, we introduce our simple yet effective token pruning method for efficient spiking transformers. We first present the heuristic spatiotemporal information-retaine -ettannnnnn criterion that assesses token importance. Then, we introduce the information-reteeniin pruning architecture with the block- level early stopping strategy. By integrating these two components, our SP-Spiiformer achieves improved efficiency, scalability, and effectiveness.
4.1 HRURISTIC SPATIOTEMPORAN INFORMATION CTOORMATTON-RETAININGG CRITERION
Extensive neuroscience research has shown that the human visual system does not process all information equally, but instead prioritizes regions that are spatially salient or exhibit significant temporal changes Itti et al. (2002); Fecteau & Munoz (0066). This selective mechanism allows biological systems to allocate computational resources efficiently to informative regions of visual input Koch & Ullman (1987). Inspired by this, we propose the IRToP to guide token pruning in spiking transformers, which assesses tokens based on their information content, giving tokens with richer spatial and temporal information higher preservation priority.
Spatial token scorer. the human visual system, spatial locations compete for saliency within feature maps, allowing only those that quite differ from their local surroundings to persist for further processing [tti et al. (2002). This motivates us to assess the representational divergence between each token and its spatial neighbors, assigning higher retention scores those with greater spatial saliency.
In the spiking transformer framework, feature representations are structured either as spatial feature maps x E Zx x or in their flattened form X 1 RTxNxD where N И W is the total number of tokens. Consider a spatial feature map at time step f, for each token located at spatial position (h, w), i.e., X E RP,, we compute the dissimilarity between this token and representative one in its spatial window. The spatial dissimilarity of single token is computed as,
(XL-1 YÉ-1 A.hap
Sucora (x = M.
tham
Y 1,b,
Σ
X--1 (p..
(10)
Wh.w
(p.g)EW,..r
where the representative token Y
1,h,w is the mean representation within the spatial window, capturing the local contextual information around the target token. Unlike pairwise similarity calculations with neighbor tokens, using this representative token reduces computational complexity. WT. is the set of valid neighboring positions within the le x l window centered at the coordinate (h, w). defined as,
k: -
Wh. = ((h
()) n H - 1] x [0, W
-
(11)
2
We calculate the dissimilarity for each token in the feature map at time step and normalize these values obtain the spatial saliency score. Each score constrained between 0 and 1, with the of
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Informative tokens
IR-Are
llasification
X
Uninformative tolens
Blo clovkelvvel
Early Stopping
sim
TopK
Scoore
5 pattal
IRToP Criterion
11
18
JPW
H
H
U
Temporal
4
Figure 2: The overall workflow of the proposed TP- TP-Spiiformer, including the information-retennn token pruning framework (top) and the spatiotemporal informatiin-retainig criterion (bottom).
all scores equal to 1. higher score indicates greater spatial saliency of a token, and these tokens are given higher priority for retention in token pruning. This ensures that the most spatially informative tokens of the original feature maps are preserved as much as possible during token pruning. Notably, this approach can be easily extended frameworks with flattened token representations.
Temporal token scorer. Neuroscientific research has demonstrated that the human visual system is highly sensitive sudden and significant temporal changes/Rensink] (2002); Nothdurft (2000). This processing mechanism serves an efficient information compression strategy, enabling the brain swiftly locate and process key temporal dynamics within extensive visual input. Inspired by this, we measure the temporal dynamics of each token between consecutive time steps and assign higher retention scores tokens exhibiting significant temporal variations. Considering a token at position (h, w) in the spatial feature map, we compute its temporal variation as,
x(-1
if
Tacore t.h.
(12)
(.h.w
if =
We calculate and normalize the temporal variation of all tokens time t obtain their temporal token, leading to higher
variation scores. Higher scores indicate richer temporal information retention priority during pruning. This biologically inspired method allows spiking transformers capture critical temporal features of input sequences while filtering out redundant information.
IRToP criterion. We combine the normalized spatial saliency and temporal variation scores for each token to obtain a spatiotemporal score at each time step, as detailed in Figure 2 Formally, for token located position (h, w) at time step our IRToP criterion evaluates this token as follows,
IRToP(X (,,,) Sucore (x.ore (X Fecore (p,,))
(13)
where S and 7 denotes the normalized score. Given the ê-th block's pruning rate тτ, clas- sify tokens in the input feature map X into informative and non-informative tokens based on their scores. Specifically, we denote a set of token scores x at time step t as Ts
[IRToP(XX=1 1,h, M== so the sets of informative tokens If- and uninformative tokens U - is:
H,W H,W
1e-- [x (h, w) TopK(TS{--)),
(14)
U fx {(h, )) /= & TopK(TS)-")),
(15)
where K - (11 WT is the number of tokens retain and TopK(.) returns the highest- scoring token coordinates. The tokens in U candidates for token pruning. In summary, the IRToP criterion offers neuroscience-imgpin inspired heuristic approach for token pruning in spiking transformers, enabling computational efficiency while retaining critical information.
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4.2 INFORMATION- RETIIOIIO TOKEN PRUNING ARCHITECTURE
After categorizing informative and uninformative tokens based on the IRToP criterion, we propose an IR-Arc for token pruning in spiking transformers. The forward propagation of the (-th block at time step L in our approach is described by the following formulas:
I U {IRToP (x H.W
(, ) h=1,
X E E #xxxD
(16)
r = ST + 1
I 1 E R
(17)
X inf MLP I'
I p-1 E R
(18)
X Luii U
U E R(HxW-R)xD
(19)
X Reesseembeee.... (X umn)
X #xxx>
(20)
In IR-Are, informative tokens undergo complete SSA and MLP to further extract the essential features, while uninformative ones skipped via block-level early stopping. All tokens are then reassembled into their original positions to restore the feature map size. Unlike direct token removal, IR-Arc skips the calculation of uninformative tokens and then keeps them unchanged. This not only reduces memory and computational overhead, but also retains more information during token pruning. Additionally, the retention and reassembly strategy allows TP-Spikformer to be easily extended to hierarchical spiking transformers, like QKFormer Zhou et al. (2024a), detailed in Appendix F
We summarize the workflow of TP- in Algorithm 1 and its advantages from two aspects. First, the information--etention strategy optimally allocates computational resources to tokens with high information content, enhancing efficiency without compromising model's feature extraction capabilitics. Second, it reduces memory and computation cost by skipping uninformative tokens rather than removing them directly, ensuring compatibility with models with feature pyramids.
Algorithm 1 The overall workflow of Spiifiiforrrr.
Require: Trained spiking transformer model; Token pruning ratio per block r r= (ri, , rr } Input image I. Ensure:
Classification results, with token pruning performed in forward propagation. X (mnuuddEbbdd))g(()
for t
1 to T do
for f 4 to L do
> Get the input feature map: X and define aeg_kernel with shape (D, D, k. k] and value I;
> Gct representative tokens from each k x l spatial window: Y!- + Conv2d(X X avg kernel);
> Token scoring: TS. SpatialScorer [ x(-1) + TemporalScorer (x x)=
>
Select the K most informative tokens: I
(x
t.h.w (h, w)) E TTp })}
> Get the remaining uninformative tokens: - +- All Tokens' \ Informative Tokens
Extract and retain important information: F SSA I X MLP((T)) + +I,;;
> Early stopping for uninformative tokens: x U 1
> Reassemble tokens restore the original feature map size: X + Reassemble X int,, X (,an));
end for CH(GAP(X!)):
Y
end for
EXPERIMENT
In this section, we conduct extensive experiments to assess our method. First, we evaluate TP- Spikformer's 's efficacy and efficiency on various architectures and tasks, comparing it with related work and uncompressed counterparts. Second, we study the zero-finetie paro-finetuning accuracy preservation property of Spikforrormrr. Third, we quantify the actual efficiency gains achieved by TP-Spikformer in training time and memory cost. Finally, we conduct ablation studies to validate the efficacy of IRToP and IR-Arc. We also visualize temporal and spatial scores of TP-Spikformer to provide insights into their operational mechanisms. Appendix A|provides details about experimental setups.
5.1 PERFORMANCE COMPARISON
Image classification. We first compare TP- TP-Spikformer with existing SNN token pruning methods. Existing methods are mostly validated on the Spikformer and small datasets, and we compare TP- Spikformer with them in Table and Clearly, TP-Spikformer maintains high performance even

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Published as a conference paper at ICLR 2026
Table 1: Comparison of TP-Spikformer on small-scale datasets. 'S' means the method doesn' 't add extra parameters or need retraining. Nwg is the average token retention ratio. t is an estimated token retention ratio based on reported metries. Top three results are highlighted as first, second, and third.
CIFAR-10 10
CIFAR-106 100
DVS CIFARIO
Method
T
Nacg
Acc. (%)
7
Nave
Acc. (%)
T
Naco
Acc. (%)
Spiktormer Zhou et al.
4
x1
95.
Baw
4
x1
78.2 Base
16
x1
80.9 Base
x
4
19.118 (.01))
4
"0.85
77.70
(463))
"6"""X
"0.85
79.3 (-16)
SparseSpikfommerr
Liu et al. 2024
x0.70
95.03 (4.16)
4
x0.70
77.07 (-114)
16
x0.70
78.4 (-2.5)
x0.63
94.77
(042)
4
x0.63
76.78
(1.43)
16
x0.63
79.1 (-18)
AT-SNN Kang et al.
2024
x0.28
95.06
(-0.3)
4
x0.75
78.14 (4077)
x0.21
94.88
(0.30)
x0.58
77.27 (4.94)
STATA Zhuge et al. 2024
4
x0.50
95.00
(0.19)
4
x0.50
77.70 (4:51)
16
x0.50
80.7 (-0.2)
TP-Spikformer
/
/
x0:20
x0.25
95.16
95.12
(0:3))
(4007)
x0.55
x0.60
78.48
77.83 (-0.38)
(-0.27)
16
16
x0.50
x0.78
81.0 (-0.1)
80.7 (40.2)
Table 2: Comparison of TP--Spiformmer ImageNet. 'Thr' reports model throughput one A800.
Method
Architecture S
T
Nacy
OPSatock (G) Power (mJ)
Acc. (%)
Thr (imgs/s)
SEW Fang et al. 2021 Spikformer Zhou et al
SEW-ResNet-155
4
x1
12.891
69.26
Spikformer-8-
4
x1
18.91
21.48
74.81
229
SNN- VIT Wang el al. 2025
SNN-ViT---512
x1
35.75
80.23
378
STATA
Zhuge et al. 2024
Spikformer 8-766
4
x0.501
11.16
74.03
4
x1
9.04
Base
10.26 Bose
76.32
Basc
156 Base
SDT-VI-8-768
XU.74
6.75
(.25%)
8:20 (1199))
75.82
(4.50)
18I (716%)
NewrIPS25 Yao et al 2023
4
x0.65
5.93
(_34))
7.46 (1269)
75.62
189 (121%))
/
4
x0.51
4.71
(48%)
6.36 (
74.79
(1.53)
202 (129%)
x1
15.08
Buse
32.12 Bsoe
85.56
Base
75 Bose
TP-Spikformer
QK-10
10-10-755 768
x0.72
10.7
(.29%)
28.18
(1129%)
84,45
11.10
(112%)
WSer 7hing ei 2024
x0.65
9.61
(,3%)
27.19
(115%)
84.32
(:124)
88
(117))
4
x0.53
7.97
(.79)
25.71
(120%)
82.53
(1300)
106 mlI
1x4
x1
1.74
Base
5.47 Base
79.72
Base
1362 Base
SDT-V3-
TX4
XU.78
(.21))
4.68
(11456)
79.01
1470
1783
(1148))
TPAMI25 Yun al.
2025
1x4
x0.65
1.13
(L38R)
4.43
(1199)
78.10
1851
(119))
1x4
x0.56
0.98
(.44))
4.25 (1229))
77.55
(2217)
1886 (121
under high compression ratios, e.g., retaining only 20% tokens on CIFAR-10 with merely a 0.07%
accuracy drop. Then, assess TP- Spikffoforrrr Imagenet- 1K and various architectures, focusing on accuracy, block operations (OPSslock), power, and throughput. As shown in Table 2 reducing tokens greatly lowers OPSslock and power, with minimal accuracy loss. For instance, retaining only 53% tokens in QKFormer cuts OPSplock by 47% power by 20%, while maintaining 82.53% % accuracy. This indicates TP-Spikformer serves an effective and general token pruning method for SNNs.
Semantic segmentation. We use ADE20K to assess the efficacy of TP-Spikformer in semantic segmentation tasks. This comparison aims to show SP-Kpikfors'r's competifiveness despite loken pruming, not its superiority in performance. We use TP- TT-SSikformer with SDT-V3 and Nazg of 0.78 and 0.56 as the backbone for feature extraction, and other settings follow Yao al. (2025). Results and visualizations are shown in Table and Figure 3al With only 56% tokens retained, TP-Spikformer achieves a 1.7x throughput with only 0.2% mloU drop compared to the uncompressed SDT-V3.
Ground Truth TP-Spikfermer
Ground Truth
TP- SpiSpikfemmer
Graund Truth
OSTrack
STARK
TP-Spikfomer Ours))
ediban
edibann
114S3
#1592
1655
(a) Semantic segmentation
(b) Object detection
(c) Event-based object tracking
Figure 3: Visualization of ground truth and ours, showing its efficacy on diverse downstream tasks.
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Table 3: Segmentation result on ADE20K.
Table 4: Object detection results COCO 2017.
Method
N Navg
T
Param (M)
Thr (imgs/s)
MloU (%)
Method
Nuvg
Param (M)
Thr (imgs/s)
mAP @0.5(%)
x1
16.5
59.6
33.6
(im et al
2020
3500
10.2
25.7
Yao et
58.9
37.6
34.8
et
2022
512
17.1
45.3
x1
58.9
36.8
35.3
Zhang et al
2023
4
11.3
28.5
x1
11.0
82.7
31.9
SSu
2023
26.9
50.1
2025
x1
11.0
74.5
40.1
Yao el al.
34.9
32.6
44.0
x1
11.0
70.4
41.4
Yao
2025
x1
38.7
29.8
58.8
TP-Spikformer x0.78
11.0
112.2
40.2
TP-Spikformer x0.78
38.7
42.9
55.4
TP-Spiktormer x0.56
11.0
128.6
40.0
TP-Spiktormer x0.56
38.7
43.6
54.4
Object detection. We COC02017 evaluate the efficacy of TP-Spikformer, which also aims to show its competitiveness under token pruning instead of superior mAP. We also use SDT-V3 with Navg of 0.78 and 0.56 as the backbone, and others follow Yao et al. (2025). Results and visualizations are shown in Table 4 and Figure 3b With only 78% tokens and fewer time steps, TP-Spikformer reaches 1. 1.4x throughput with only a 1% mAP drop, showing its efficacy in object detection.
Event-based tracking. We select the event-based tracking task to verify the effect of TP- TP--ppiformme in sequence vision tasks. Experiments are conducted on three benchmarks, i.e., FE108 Zhang et al.) (2021a), FELT Wang et al. (2024a), and VisEvent Wang et al. (2023). Similar in segmentation and detection, we use SDT-V3 with Nang of 0.78 and 0.56 as the backbone, and other settings follow Shan et al. (2025). Results and visualizations are shown in Table 3 and Figure 3c Using only 56% of the tokens, Tp-Spikformer surpasses most RGB-based trackers and rivals the advanced SDTrack, demonstrating its effectiveness in sequential vision tasks.
Table 5: TP-Spikfommer vs. advanced trackers on three event- event-based object tracking benchmarks.
FE108
FELT
VisEvent
Time step
Power (mJ)
Methods
Navy
AUC(%)
PR(%)
AUC(%)
PR(%) %)
AUC(%)
PR(%)
STARK Yan et al.
2021
x1
58.88
57.4
89.2
39.6
51.7
34.1
46.8
SimTrack
Chen
2022
x1
93.84
56.7
88.3
36.8
47.0
34.6
47.6
OSTracko5 Yc al.
2022
x1 98.90
54.6
87.1
35.9
45.5
32.7
46.4
ARTrack25 Wer al.
2023
x1 174.8
56.6
88.5
39.5
49.4
33.0
43.8
BeqTrack-Bas
Chen al.
2023
x1 302.7
53.5
85.5
33.0
42.0
28.6
43.3
HiT-B Kang
20233)
X1 19.78
55.9
88.5
38.5
48.9
34.6
47.6
HIPTrack
2024
X1
307.7
50.8
81.0
38.2
48.9
32.1
45.2
ODTrack
Zheng
X1
335.8
43.2
69.7
29.7
35.9
24.7
34.7
STNet Zhang et al.
2022
3
50.3
SDTrackriuy Shaa ct al.
2025
4
X1
8.16
59.0
91..3
39.3
51.2
35.6
49.2
TP-Spiiformer
4
x0.65
6.65
59.0
91.22
39.1
50.4
35.3
49.7
TP-Spikformer
4
x0.56
6.51
58.4
90.6
38.9
50.0
35.2
49.4
SIFEVI
OKFmmrr
SDT-V3
F6.32
74,70
Ascuracy
75.38
75,48
83.79
75.95
60
64.38
67.03
Tiazure
Tinaure
Finaxee
40
40
Fister
Uapoased
Faster
20
156
181
189
202
75
84
106
1562
1785
1851
1886
Throoghhpt timgsis)
Throughoptt (imgsis)
Throughput (ingsis)
24
10
0.9
24
Traning
Tane dligeet
Traning
Training Time
22
0.8
22
22
8-
20
6
20
20
0.16
0.36
0.5
0.72
1.0
0.16
0.36
0.5
0.72
1.0
0.16
0.36
0.5
0.72
1.0
Token prune ratio No.
Token prune ratin New.
Tokan prume rutin N
Figure Zero-finetuning accuracy preservation (top) and efficiency gains (bottom) of TP-Spiiformerr.
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Table Ablation study, Random. denotes random token pruning; Drop means token removal that reduces feature map size; Spat ial and Temporal is using one single scorer for token selection.
Architecture [Random, Drop] [Random, IR-Arc] [Spatial, IR-Are] [Temporal, IR-Are] (IRTeP, IR-Arc]
SDT-V x0.02
59.88%
60.02%
73.52%
70.95%
73.78%
OKFormexxo.4 SDT-V3
Fail
74.45%
58.93%
79.69%
81.16%
73.15%
75.95%
75.95%
5.2 VALIDATION AND ABLATION STUDY
Zero-finetuning accuracy preservation of TP-Spikformrr. We observe that TP-Spikformer can also perform well in a training-fre manner. To verify this, we prune three architectures using official pre-trained weights, comparing results with and without fine-tuning. As shown in Figure a),, TP-Spikformer attains high accuracy without fine-tuning, showing its simplicity and generalization. This makes it well-suited for real-world scenarios with limited resources and retraining budget.
Speedup and memory improvement of TP-Spikformer. Besides inference throughput, we quantify the efficiency gains of TPP-Spikfomerr in training. Experiments are conducted on ImageNet, measuring training time and memory usage. These metrics are tested on a single NVIDIA 4090, with the batch sizes of SDT-VI, QKFormer, and SDT-V3 set to 20, 15, and 200. As shown in Figure bb) TP- Spikformer notably reduces both training and memory cost as the token retention ratio decreases.
Ablation study of IRToP and IR-Are. We conduct ablation studies ImageNet without fine-tuning, assessing IRToP and IR-Arc. Table 6 summarizes results, with key findings outlined below. First, IRToP outperforms Random token pruning under IR-Arc, with gains of 13.76%, 6.7 1%, and 2.8% on SDT-V1, QKFormer, and SDT-V3, respectively, showing its efficacy. Second, the efficacy of IR-Are is shown by comparing with Drop; though their gap is small SDT-VV (59.88% vs. 60.02%), IR-Arc better supports varying feature map sizes, like QKFormer. Third, decoupled analyses of Temporal and Spat Maliill show that the Spat ial scorer suffices on SDT-VI, while Temporal is more important on QKForeer, showing that both scorers in IRToP are indispensable.
Spatial
Tempoml
IRToP
Spatial
Temporal
IRToP
61
Figure 5: Visualization of spatial and temporal token scores in the 8th block of SDT-V1-8-766
Decoupling analysis IRTOP. We decouple and visualize spatial and temporal scores to understand their roles. Figure shows token scores from the last block before the classification head, where the spatial scorer assigns higher scores to tokens related to the main subject, while the temporal one emphasizes edges and key parts (e.g., claws, wings, beak). We find the temporal scorer underperforms at time step 1, likely due to the large magnitudes of background tokens causing misidentification. This also explains the poor token pruning results at the first time step Figure ! We thus recommend using only spatial scores at the first step and combining both from the second step onward.
6 CONCLUSION
Existing transformer-based SNNs integrate transformer performance with SNN efficiency, are constrained by increased model size and computational demands. This paper presents TP-Spikformer, simple yet effective token pruning approach for spiking transformers that reduces memory and computation overhead. Drawing inspiration from human visual processing, TP-Sppiformer imple- ments the IRToP criterion and IR-Arc architecture, striking excellent balance between efficiency and performance across multiple architectures and tasks. Extensive experiments and comprehensive studies demonstrate its value in real- wollwwrldd scenarios with limited resources and no retraining budget.

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ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (Grants 62576080 and 62220106008), the Sichuan Science and Technology Program (Grant 2024NSFTD0054) the Guangdong Introducing Innovative and Entrepreneurial Teams (Grant 20232T10x044), and the Shenzhen Science and Technology Research Fund (Grant 00Y11202208111000001000
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A EXPERIMENT DETAILS
A.1 IMAGE CLASSIFICATION
Dataset We evaluate TP- -P-Spiiformer on both static and dynamic datasets. The static datasets include CIFAR-10, C10, and the large-scale ImageNet- K. CIFAR-10 is widely used benchmark in computer vision, containing 10 categories with 6,000 x2 images per category Krizhevsky et al. (2009) CIFAR-100 maintains the same image size but expands the categories to 100, which are organized into 20 superclasses KreercaasssKKizhevssky
al. (2009). ImageNet-IK, is large-scale vision dataset, featuring approximately 1.28 million training images and 50,000 test images across 1,000 categories Deng al. (2009). Its diverse categories and rich image content make it critical benchmark for image classificatio.. Additionally, DVS-CIFARE is a dynamic dataset derived from CIFAR- 10 using dynamic vision sensor [i et al. 2017). This dataset includes 9,000 training samples and 1,000 test samples, with temporal resolution in the microsecond range and spatial resolution of 128x 128.
Experimental setup For small- scale datasets CIFAR and DVS-CIFAR 10, we perform exper- iments with two token pruning ratio settings. Specifically, Navg is set to 0.25 and 0.20 for CIFAR-10. 10, and 0.65 and 0.55 for CIFAR-100. We use the Spikformer-4-386 4-4 as used in stud- ies Liu et al. (2024): Kang et al. (2024); Zhuge et al. (2024), fine-tuning with learning rate of 5e-5 and time step 4. For DVS-CIFALE CIFAR1O, we set Nave 0.78 and 0.5, using the Spikformer-2- 384 and fine-tuning with learning rate of 7e-4.
Table 7: Token preserving ratio small datasets.
CIFAR-10
CIFAR- 100
Block
0.25
0.20
0.60
0.55
0.78
0.50
1
0.76
0.56
0.76
0.76
0.78
0.76
2
0.14
0.14
0.76
0.76
0.78
0.25
3
0.06
0.06
0.76
0.56
4
0.06
0.06
0.14
0.14
Other experimental settings follow prior work.
For the large-scale ImageNet, we conduct experiments with three pruming ratios for each structure, detailed in Table 2 We summarize the token retention ratio per block of small datasets Table 7 and Imagenet-Ie KK in T8 In the next paragraph, we introduce how the token retention ratio per block obtained. During fine-tuning, we remove the warm-up epoch and set the learning rate to le-5 for 50 epochs. The batch size per GPU for SDT-VI I and QKFormer is 72, while 760 for SDT-V3, The detailed settings for ImageNet are shown in Table D all experiments, when calculating the spatial scores of tokens, we compute the set of neighboring positions in 3 x window, i..,, k 3. We also discuss the effect of on performance in
Table 8: Token preserving ratio in each transformer block Imagenet- IK.
SDT-VI
QKFormer
SDT-V3
Block
0.74
0.65
0.51
0.72
0.65
0.53
0.78
0.65
0.56
1
1
0.73
0.90
0.81
0.64
0.81
0.64
2
0.73
0.73
0.90
0.81
0.64
0.90
0.81
0.64
3
0.73
0.73
0.51
0.72
0.64
0.64
0.90
0.64
0.64
4
0.73
0.73
0.51
0.72
0.64
0.49
0.90
0.64
0.64
5
0.73
0.51
0.51
0.72
0.64
0.49
0.64
0.64
0.49
6
0.73
0.51
0.51
0.72
0.64
0.49
0.64
0.56
0.49
7
0.51
0.51
0.32
0.64
0.64
0.49
0.64
0.56
0.49
8
0.51
0.51
0.32
0.64
0.56
0.49
0.64
0.56
0.49
9
0.64
0.56
0.49
10
0.64
0.56
0.49
In order to find the optimal pruning combination between blocks, employ search strategy before fine-tuning to determine the token pruming ratio per block based on the given global pruning rate. The grid search used is very simple method, which is intended to perform a coarse search to initially identify a reasonable pruning rate. Given a pre-trained model and a global token preservation, we summarize its detailed search process below.
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Table 9: Experimental setups on Imagenet- IK.
Hyper-parameter
SDT-VI
QKFormer
SDT-V3
Navg
0.74, 0.65, 0.51 0.72, 0.65, 0.51 0.78, 0.65, 0.56
in IRToP
Time step
Warmup epoch
None
None
None
Epoch
50
50
50
Resolution
x24x224
224x224
224x224
Batch size per GPU
72
72
760
Optimizer
Adam
Adam
Adam
Weight decay
Initial learning rate le-5
1e-5
1e-5
Learning rate decay
Cosine
Cosine
Cosine
First is to obtain set of token preservation ratio combinations. Specifically, the search space for ratios is restricted to a small set of discrete values, such as 0.9x0.9, 0.8x0.8 8, 0.75x0.75, 0.6x0.6, etc. Furthermore, token preservation ratio to decrease progressively from shallow to deeper blocks. This is motivated by the observation that shallow layers capture low-level features and thus require higher token retention, while deeper layers handle high-level semantic information and can tolerate more aggressive token pruning Lin al. (202I).
impose monotonic constraint, requiring the
Second, randomly sample small batch of data from the training dataset and evaluate the accuracy for each combination in the combinations set. The combination with the top- accuracy is selected and used for subsequent fine-tuning.
By reviewing the search logs, we observe that different configurations give similar performance under the same global ratio. Therefore, the grid search is only used for SDT-V3, while for QKFormer and SDT-V1, directly set the ratios manually and fine-tune the models without performing grid search. We summarize the search details for SDT-V3 in Table TO] including the discrete search space, the number of candidate combinations, the time to evaluate each combination, and the total search time.
Table 10: Search details for the SDT-V3.
Searching space per token retention ratio
Evaluation time combinations per combination
Number of
Total Time
Model
Nang
(4*NVIDIA 4090)
SDT-V3 0.78 IL,090.081007220.444066644444449
65
26s
28min 11s
SDT-V3 0.65 10.8.0772.0644055040000000000
89
22s
32min 38s
SDT-V3 0.56 (0.11077200444,55044904000.....
166
16s
44min 16s
A.2 SEMANTIC SEGMENTATION
Dataset ADE20K Zhou et al. 2019) is a widely used and well-established dataset for semantic segmentation in computer vision research. It comprises approximately 25,000 images, with over 20,000 images designated for training, 2,000 images for validation, and 3,000 images for testing. Each image in the dataset is densely annotated with pixel-level labels across 150 distinct semantic categories. These categories cover a wide array of objects, such as people, cars, and animals, as well as scene elements like sky, roads, and vegetation, each with intricate visual features that make semantic segmentation tasks more challenging. Due to its diversity and complexity. ADE20K serves as critical and challenging benchmark for evaluating the performance of segmentation algorithms.
Experimental setup In this work, begin by converting the mmsegmentation Contributors (020) codebase to its baske version, inspired by the SDT-V3 Yao et al. (2025). We employ TP. Spikformer with SDT-V3-19M the backbone for feature extraction, integrated with spike FPN
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(Kirillov et al. 2019) for segmentation. The backbone is initialized using pretrained weights from ImageNet, ensuring that the network has a strong starting point for feature extraction. The newly added layers are initialized using the Xavier method Glorot & Bengio (010)) The experimental settings follow the parameters set in SDT-V3 to ensure consistency and comparability. We fine-tune 4x with batch size of 12 per GPU, while original SDT-V3 is limited to 8. The results in Table 3 show that our method maintains the performance of SDT-V3 and greatl increases throughputs. This comparison is not intended to demonstrate that our method achieves top-1 I accuracy, but rather to highlight that our approach remains competitive even under token pruning conditions.
the model with two ratios same object detection
A.3 OBJECT DETECTION
Dataset We evaluate TP-Spikformer COC02017 Lin et al. (2014), large-scale benchmark that is widely used for object detection tasks. The dataset comprises a total of 118K training images, 5K validation images, and 40K test images, providing a comprehensive and diverse set of visual data. It covers 80 object categories, including everyday items such as cars, bicycles, animals, and household objects, which are essential for testing the algorithm's 's ability to recognize and interpret a wide range of visual content. In addition to object categories, COCO offers multiple types of annotations, including object instance segmentation masks, keypoints, and captions, all of which contribute to the dataset's robustness for evaluating various vision tasks. Notably, COCO emphasizes contextual relationships between objects within complex, everyday scenes, offering more realistic and challenging evaluation setting compared to simpler datasets. This makes COCO crucial benchmark for assessing the performance of computer vision algorithms in real-world, practical applications.
Experimental setup Similar to semantic segmentation, we begin by converting the mmdetection Chen et al.] (2019) codebase to its spike-based version. Our model architecture integrates TP- Spikformer with Mask R-CNN (He et al. 2017). The backbone is initialized using pretrained weights from ImageNet, and the newly added layers are initialized using the Xavier method GGloot & Bengio 2010). We fine-tune the model with two different average token retention ratios: 0.56 and 0.78. These two settings allow us to explore how different levels of token retention influence the model's 'ss performance in object detection and segmentation tasks. The experiments are conducted on a x A800 setup, with a batch size of 5 per GPU, providing ample computational resources to handle the large-scale training process. The results are summarized in Table which also highlights that our approach remains competitive in object detection tasks.
A.4 EVENT-BASED TRACKING
Dataset We use three event-based tracking benchmarks assess our TP-Spifkormer, detailed as:
FE108 is captured by the DAVIS346 dynamic vision sensor, with event rate spanning range from 0 to 3800 events/ms Zhang et al. (2021a). This dataset features 21 diverse target categories. The diversity of categories and the high event rate make FE108 particularly useful for evaluating event-based models under varying conditions.
FELT Wang et al. (2024a) is specifically designed to address the challenges associated with long-term object tracking in dynamic environments. This dataset places a strong emphasis on scenarios where the loss and recovery of targets are crucial for maintaining tracking accuracy.
VisEvent Wang al. (2023) is a large-scale dataset dedicated to event-based visual tasks, of- fering a robust testing ground for various event-driven models and algorithms under extreme conditions. With its broad scope, VisEvent includes a wide range of event-based visual tasks, providing a unique and challenging environment for assessing model performance.
These datasets serve as three of the most important benchmarks in event-based tracking. Their diversity makes them important for evaluating the performance of event-driven models and algorithms.
Experimental setup We use the SDTrack pipeline to build a tracker for event- based tracking tasks Shan et al. (2025). Specifically, train the tracker using image pair matching task Chen et al.
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Yan et al. (2021) Ye et al. (2022) and employ weighted focal loss Law & Deng (2018) for cassification. For the predicted bounding boxes, L1 loss and generalized IoU loss Rezatofighi et al. (2019) are used for bounding box regression. We train the model for 100 epochs on the FE108 and VisEvent datasets, using a pretrained ImageNet-1 model, and for 300 epochs on the FELT dataset. For each training epoch the FE108 and FELT datasets, we randomly sample 60,000 sample pairs with a maximum interval of 200, while on VisEvent, we use 30,000 pairs. The learning rate used during training is 4e-4, decaying 4e-5 at 80% of the training progress. We apply normalization and regularization on the FELT dataset, and a Hanning window penalty is used to constrain the predicted boxes, However, no data augmentation or preprocessing is applied the FE108 and VisEvent. All of the above experimental settings are strictly aligned with SDTrack [Shan et al. (2025)
(202
B MEASUREMENT OF EFFICIENCY METRICS
Table 11: Training time and memory usage of TP-Spikformer under different ratios and architectures.
SDT-VI
QKFormer
SDT-V3
N avg
Memory
Training
Memory
Training
Memory
Training
usage (GB) time (h/epoch)
usage (GB) time (h/epoch)
usage (GB) time (min/epoch)
x1
23.19
7.47
22.80
8.78
23.33
50.27
x0.72
21.44
6.22
21.03
7.11
20.96
42.16
x0.50
20.21
5.51
18.40
5.93
19.08
38.96
x0.36
18.76
4.98
16.37
5.45
18.25
36.29
x0.16
18.68
4.62
14.92
4.98
16.42
34.16
As shown in Figure 4 we report the training time per epoch, GPU memory usage, and inference throughput for SDT--V,, QKFormer, and SDT-V3. In this section, provide detailed description of how these metrics are measured, including the measurement method, experimental setup, and results.
The training time per epoch and GPU memory usage measured by monitoring the time taken to complete one epoch and the GPU memory consumption during training. These metrics are tested on single NVIDIA 4090, with batch sizes fixed 20 for SDT-V1, 15 for QKFormer, and 200 for S3T-V3. We measure these two metrics of Tpi-Spikfrrmer under different token pruning rates, with the results summarized Table II is evident that the proposed -PiSpifforerr significantly reduces both training time and memory consumption, resulting in substantial efficiency gains, As result, under the same configuration, when maximizing GPU resource utilization, TP-Spikformer typically allows for a higher batch size compared to its uncompressed counterpart.
For the metric of inference throughput, we estimate by calculating the number of images processed second during the inference process. The throughput values reported in Figure Tables measured on a single NVIDIA A800 GPU, with batch size of 36 for both SDT-VI and QK Forrmerr, and 1024 for SDT-V3 in the classification task. In the case of segmentation and detection tasks, the batch size is fixed to 1. The obtained throughput results, presented in Tables 2 to 4 display that -P-Spikformer achieves higher throughput across all models while maintaining high accuracy. This enhances the efficiency of model inference and faster real-time processing, making it more suitable for deployment in resource-constrained scenarios that require real-time processing.
C ANALYSIS OF k IN THE IRToP CRITERION
In this section, we evaluate the impact of different values of i on the performance of Spi-Sprffommer, specifically SDT-V1, QKFormer, and SDT-V3, on the ImageNet-1K dataset under zero pero-finetuning conditions. The results are summarized in Table 12 Each architecture shows stable performance across the different values of k, with slight decrease in accuracy as k increases. This drop in performance may be attributed to the fact that larger spatial windows, while capturing more global context, reduce focus important local details, which is crucial for tasks like fine-gu tineggrined detection and segmentation. Therefore, we select & = 3 for the experiments presented in the main text, as offers good balance between computational efficiency and effectiveness.
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Table 12: Performance of TP-Spifkormer with different on ImageNet-IK IK without finetuning.
SI>T-V1
FKrmme
SDT-V3
value
0.74
0.65
0.51
0.72
0.65
0.53
0.78
0.65
0.56
3
75.38
75.48
73.78
83.79
81.34
64.38
75.95
67.03
60.19
5
75.37
75.48
73.77
83.65
81.23
64.18
75.77
66.59
59.71
7
75.34
75.48
73.77
83.66
81.20
64.32
75.59
66.47
59,40
D ADAPTIVE WEIGHTING OF SPATIAL AND TEMPORAL SCORERS IN IRTOP
we have explored adaptive weighting between spatial and temporal components to analyze the effects of adaptive weight in the IRToP criterion. Specifically, we introduce a learnable parameter a to balance the spatial and temporal scores adaptively. The modified IRToP criterion formulated as:
As for Eq. [3]
(X (,h,w = Sacore ( + a)) Tscore (.h,,a)):
(21)
where is initialized to 0.5 (equal weighting) and is differentiable, allowing it to be optimized during training. This enables the model to automatically learn the optimal balance between spatial and temporal importance. We conduct experiments ImageNet- K using SDT-VI with pruning ratio of 0.51, fine-tuning for 50 epochs. The results are summarized Table 13 Compared to fixed equal weighting (a 0.5), observe a slight performance drop when using the adaptive weighting method. We suspect this is because the learned value of a converges to around 0.3, suggesting that the model tends to emphasize temporal features over spatial ones. In this case, the model may overly focus on critical temporal dynamics and local details, potentially at the expense of broader spatial context that essential for robust feature representation, leading to the observed performance degradation.
Table 13: Analysis of adaptive weighting of spatial and temporal scorers in IRToP.
Fine-tuning accuracy under adaptive a
Fine-tuning accuracy under fixed a=0.5
Model
Ratio
Final ratio
SDT-V1
0.51
0.3
74.23%
74.79%
E DETAILED ANALYSIS OF IRTOP
t=2
Mook1
Spelial
Temponal
Figure Detailed visualization of spatial and temporal token scores SDT-VV-8-768.
IRToP proves effective by selecting informative tokens with two key characteristics: tokens that represent the overall outline and tokens that capture specific detail features. As supplement
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Figure 5 we further analyze the scores of tokens selected from the 1st, 2nd, and 7th encoder blocks. In Figure 6 we decouple and visualize their spatial and temporal scores. From the spatial scores, is clear that, irrespective of the time step and block, the spatial token scorer assigns higher scores to tokens representing the main outline, followed by those representing specific details, and lastly, background tokens. In terms of temporal scores, the temporal token scorer further extracts feature information by assigning higher scores to tokens that correspond to specific detail features. For example, in the 1st block & 3. tokens representing the bird's ' claws receive high scores. In the 2nd and 7th blocks at t = 3, tokens representing the head, wings, and branches are assigned high scores. Similarly, at 4 in the 7th block, tokens representing the bird's tail are given high scores,
The above visualization results can be explained by the design of the spatial token scorer and the temporal token scorer. The spatial token scorer relies on the similarity with neighboring tokens, assigning higher scores to tokens that differ significantly from their local surroundings. This is why the spatial token scorer extracts more information representing texture and boundary features. The computation of the temporal token scorer is inspired by the working mechanism of the human visual system. Specifically, tokens representing distinct features are captured over time, while background tokens are gradually ignored. As result, the temporal token scorer is able to extract specific feature information. Overall, by combining the spatial token scorer with the temporal token scorer, IRToP effectively selects informative tokens, reducing computational resources while maintaining performance.
F WHY CANNOT DIRECT TOKEN PRUNING BE EXTENDED TO THE FEATURE VARIANT SPIKING TRANSFORMER?
Direct token pruning refers to identifying uninformative tokens and discarding them without further processing in the subsequent network layers. In this section, we first discuss why this approach cannot be applied to vartint spiking transformers. Then, we use feature-variant QKFormer as an example to illustrate the issue. Finally, we explain how our method effectively solves this issue.
Early SNN transformers, e.g.. Spikformer Zhou et al.) and SDT- V1 Yao et al. (2023a), follow ViT-style designs from ANNs, using patch embedding and standard transformer blocks. As the field developed, recent SOTA models like QKFormerZZow Zhou et al. (2024a) and SDT-V2/V3 Yao et al. 2025) incorporate convolution layers with kernels larger than inside transformer blocks. For example, QKFormer applies conv-based Spiking Patch Embedding before each block, and SDT-V3 uses spike-based separable convolutions before every attention layer. Unlike ANNs where features can be flattened for token pruning, these convolutional layers embedded in the transformer blocks require structured and square feature maps for token pruning in SNNs. These feature-variant spiking transformers include many operations that reduce the size of feature maps, such as downsampling and convolution. The inherent structural sensitivity of these operations makes direct token pruning incompatible with feature-varien spiking Transformers, which can be understood from two aspects:
On the one hand, convolution operations rely on structured grid-like inputs. This means that if tokens are removed from transformer block, the remaining tokens may no longer form valid image layout, making them incompatible with later convolutional layers.
On the other hand, due its strong prior assumptions, e.g., spatial local correlation and translation invariance, the convolution operation heavily relies on the spatial structure of feature maps. However, removing tokens disrupts the spatial structure of feature maps. This disruption (1) impairs local information propagation, (2) degrades the effectiveness of trained filters, and (3) compromises the model's representational ability.
As detailed in the above two reasons, it is the existence of convolution in spiking transformers that makes direct token removal infeasible, while our block-level early stopping strategy remains viable. This also indicates that, when pruning advanced spiking transformers like QKFormer and SDT-V2/V3, it is essential to preserve the overall architectural integrity. Notably, existing SNN token pruning methods have only been tested on ViT-like Spikformer and -pike-driven transformer V1, and have not yet been applied to recent SOTA spiking transformers. To the best of our knowledge, we are the first evaluate token pruning on these advanced spiking architectures.
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We then use the advanced hierarchical transformer architecture QKFormer in SNNs an example to illustrate the above issue. In QKFormer, each stage consists the Spiking Patch Embedding with Deformed Shorteut (SPEDS) module and QKFormer block. The SPEDS module includes structure-sensitive convolution and pooling operations, which reduce the number of tokens by a x patch size before each stage and transform the number of channels into 2C generate hierarchical spiking representations. If we directly prune the uninformative tokens identified by IRToP criterion the first stage, the remaining informative tokens need be reorganized into a new feature map before being input into the second stage. This will lead to the following two challenges.
Difficulty in reshaping the feature map. The reorganizing process typically requires the remaining informative tokens to be arranged into a square feature map for efficient processing in the next stage (e.g.. 196 14x14). However, after pruning uninformative tokens in the first stage, the number of remaining tokens often cannot form the required square shape for reshaping the feature map.
Disruption of spatial structure. Even if we constrain the remaining informative tokens' count to match the square shape, the spatial structure of the reconstructed feature map is inevitably disrupted. This results in the failure of well-trained parameters in the convolution operations within the SPEDS module of the second stage. This would affect the information flow in subsequent network layers and significantly degrade model performance.
TP-Spikformer addresses this challenge by introducing a block-level early stopping strategy for uninformative tokens. Instead of directly removing tokens that would disrupt the spatial structure of the feature map, TP-Spikformer bypasses the processing of uninformative tokens within the transformer blocks, and then reorganizes all tokens spatially before inputting them into the next stage. This process reduces the memory and computational overhead associated with token pruning by bypassing the computation of uninformative tokens. Moreover, by preserving the integrity of the feature map's spatial structure, TP- TP-Spikformer avoids the difficulties of reshaping tokens and maintains the well-trained parameters of the filters, ensuring that the model maintains competitive performance without fine-tuning.
G ZERO-FINETUNING ACCURACY PRESERVATION OF TP-SPIKFORMER
The zero-finetuning accuracy preservation of TP-SSiiformerr is made in a comparative sense. Existing advanced token pruning methods in SNNs often modify the original model architecture when applied to spiking transformers. These modifications may include introducing new tokens (STATA Zhuge al. (2024)), adding trainable modules (ACT Kang al. (2024)). Since these additions are randomly initialized, they require full retraining, which significantly increases data requirements, training costs, and reduces generalizability. Therefore, though our method does not completely preserve accuracy on QKFormer and SDT-V3, it achieves better accuracy than existing spiking token pruning methods under the same no-fine-tuning setting.
In Section 5.2 we conduct an in-depth anal- ysis of SPippirfomner's zero-fine seroffinctunin performance preservation property in im- age classification tasks. In this section, we demonstrate that TP Tpikkiifrrerr exhibits this property in other vision tasks as well. We evaluate the performance of the un- pruned model using publicly available SDT- V3 detection and segmentation code and obtain results of 39,49% MIoU for seman- tic segmentation and 53.9% mAP@0.5 for object detection, respectively. Then, we directly apply our token pruning method to the obtained weights without any fine- tuning. With a compression ratio of 0.78, TP rp-Spikformer achieves performance of 36.15% and 47.7% in semantic segmentation and object detection, representing reductions of 3.34% and 6.2%, respectively. These results indicate that TP-Spikformer maintains its zero-finetuning
Validation of ST--Soiddrrme'ss zero- finetuning accuracy preservation property
A
Somantic wggmenttatoorreeooommnsssss model
39.49
segmentation Noug 0.78
36.15
Object detection:Ulccomeessses model
53.9
Object detection: Nezp
0.78
47.7
10
20
30
40
Figure 7: TP-Spikforner's zero-finetuning accuracy preservation on segmentation and detection.
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performance preservation property in various downstream vision tasks, highlighting its effectiveness for real-world scenarios with limited resources and no retraining budget.
H EFFECT OF TP-SPIKFORMER IN ACCELERATING TRAINING FROM SCRATCH
Table 14: Effect of -piSoorforerr in the training process.
Token preservation ratio
Total training time
GPU Memory (batch size=1280)
Model
Params (M)
Acc.
SDT-V3
5.1
57h14min 50h 49min
77.49 GB
73.9%
TP-Spikformer
5.1
0.65
66.90 GB
73.6%
Although our method is mainly designed to improve deployment efficiency, it can also be easily applied during training. To verify this, conduct experiments large-scale ImageNet using SDT-V3-5M with and without our token pruning method, both trained from scratch. All experiments single H800 GPU, using the same settings the original SDT-V3 ensure a fair comparison. As shown in Table 14 T--Spikformer achieves 73.6% accuracy, close to the 73.9% of the unpruned model, while reducing training time by 7.5 hours. This shows its effectiveness in speeding training. Moreover, TP- Spi-piifrrmer uses much less GPU memory due to fewer tokens, which allows larger models or batch sizes to trained on the same hardware.
VALIDITY OF TP-SPIKFORMER ON NON-VISUAL TASKS
The vision datasets used in the manuscript, like ImageNet, COCO, and ADE20K, are universally recognized as complex datasets in the fields of classification, detection, and segmentation, respectively, These experimental results prove the effectiveness of NN in complex image tasks. To further show the efficacy of our method in non-image tasks, we have conducted experiments in NLP tasks.
We extend our TP-Spikfommer the SpikeLM proposed by Xing al. (2024) without additional architecture adjustment. Experiments utilize 12-layer BERT-basce encoder transformer and are performed on the GLUE benchmark. The token preservation ratio is [1, 1, 1. 0.9, 0.9, 0.9, 0.8, 0.8, 0.8, 0.7, 0.7, 0.7]. while all other training configurations follow the original paper. The results are presented in the table below. Clearly, Up-SSpiformmr achieves performance of 75.9%, showing no significant loss. These results confirm the effectiveness of our method on NLP tasks, demonstrating its general applicability beyond the vision domain.
Table 15: Validation of TP-Spikformer on the GLUE benchmark.
Model
SST-2
MRPC
RTE
MNLI
QNLI
QQP
CoLA
STS-B
Avg.
SpikeLM
87.0
85.7
69.0
77.1
85.3
83.9
38.8
84.9
76.5
Spikfrrfrrerr
87.9
84.7
68.2
76.0
84.6
84.2
37.0
84.9
75.9
POTENTIAL OF S--IIFFFRRERR WITH OTHER COMPRESSION TECHNIQUES
Our TP- -P-Spikformer is orthogonal to other lightweight approaches and can be used in conjunction with them. Here, we investigate the combination TP-Spikformer with quantization. We select the Q-SDT proposed by Qiu et al. (2025) to evaluate this combination. Specifically, we conduct experiments on CIFAR- 10 by applymn ying P--Spiffrmmer to Q-SDT, training the model from scratch. The results shown in Table 16 While there is performance gap compared to the baseline, the 96.9% accuracy demonstrates that our token pruning method can work effectively with quantization techniques.
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Table 16: Potential of Spikkiffrrmer with quantization.
Model
Preserving Ratio
Bits
GPU Memory (GB)
Accuracy (%)
Q-SDT Qiu et al. (0025)
4-1
19.27
97.8%
Q-SDTHIP-Spikioreet
0.54
4-1
15.98
96.9%
K
LEARNING ALGORITHM OF TP-SPIKFORMER
We employ the widely used spatiotemporal backpropagation (STBP) Wu et al. (2018) Jiang et al. (2025) to train the TP-Spikformer, where we need to compute gradients of the loss function L to synaptic weights. Through chain rule decomposition, we decompose this gradient as,
Oc Ow!
Σ ac [1 (nf(t) 0s514 aul Ow
ac
aul ++] anf auf44 Owl,
(22)
0ufl++1) +1]
where the derivative of the loss function with respect to the spike and membrane potential, i.e., 0C/Os(11 and OL/Ou 1] are obtained iteratively, the terms of 14///// Ouf + 11/0ū and (tw//// can be calculated based Eq. I Unfortunately, fundamental challenge in this training arises from the non-differentiable nature of spike emission. Mathematically. the gradient of the spike generation function described in Eq. i.e., [s{ //ū becomes undefined at the firing threshold and vanishes elsewhere. This discontinuity prevents the direct application of standard backpropagation algorithms commonly used in deep learning. To overcome this limitation,
surrogate gradient functions to approximate the derivative of Ös ( ///o Wu et al. (2018), with various functions can be employed like rectangular Wu al. (2019), , triangular Deng et al. (2022). and linear Wei et al. (2024b). TP-Sppkformerr employs the triangular-shaped surrogate gradient formulation, described as,
0s [t] [
(0. 3 11 01)
(23)
where 3 is the factor that defines the range of gradient computation, and 0 the threshold as in Eq. Consequently, the TP-Spikformer can be trained directly with gradient backpropagation.
L THEORETICAL ENERGY CONSUMPTION
When analyzing the energy consumption of SNNs, previous studies Yao et al. (2023b); Zhou et al. 2024a); /Wii al. 22025a b) commonly assume that MAC and AC operations are implemented on 45mm hardware Horowitz 2014), where ÆMAC = 4.6pJ and É = 0.9p.. To facilitate compari- son between different methods, we adopt this approach theoretically calculate TP-Spikformer'ss energy consumption, described by the following equation:
Fiotal .EMAC FLOP% + FAC X
SOP Conv
xSOPs Block
Block + 100PSLLS
2=
(24)
where SOPs refers to the number of synaptic operations, S and SOPs s represent the SOPs for the convolutional operations in the embedding module and the MLP the classification head, respectively, and SOPs Block denotes the SOPs for each transformer block. The number of SOPs in SP-Spikformer computed as:
SOPS = ST'Avg x X FLOPs
(25)
where fr is the average firing rate of the layer across time steps T, and FLOPs' is the number of floating point operations for the l-th layer. spiking transformer typically consists of three components: path embedding, transformer blocks, and the classification head. In Spikffrfrr,,r, the energy consumption of the path embedding and classification head is consistent with the uncompressed counterpart, while the energy consumption of the transformer blocks is significantly reduced.
23

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Published as a conference paper at ICLR 2026
M INSTRUCTIONS FOR USING LARGE LANGUAGE MODELS
In preparing this manuscript, we utilize a large language model (LLM) solely to aid and polish the writing. The LLM is used for grammar checking, language refinement, and improving clarity of expression. It does not contribute to the formulation of research ideas, methodology, experiments, data analysis, or conclusions. All presented in this paper is entirely the work of the authors.
24


Reviews:
[{"review_id": "XKpf7CpCFB", "reviewer": "Reviewer_TA1d", "summary": "This paper introduces a token pruning method for spiking transformers. First, a heuristic spatio-temporal information-retaining criterion is proposed to evaluate token importance. Second, based on the importance scores, a block-level early stopping strategy is proposed for uninformative tokens. Experiments are conducted on Spikformer, QKFormer and Spike-driven Transformer V1 and V3.", "questions": "What's the main contributions from the methodology perspective?\n\nWhy not cite the ANN transformer sparsification papers which this paper's main techniques originate from? Similarity-based pruning has already existed, even the token-level sparsification has already been researched for years. But no citation is found in this paper.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "1. The narrative is very convincing that token-level sparsification is a very effective  way for transformer-style models to boost computational  efficiency.\n\n2. This paper's experiments are very solid and extensive. The token sparsification method is verified across different tasks and multiple datasets, which makes the effectiveness very convincing.\n\n3. This work demonstrates the most advanced token  pruning results for spiking ViT in comparison with previous spiking token  pruning methods.", "weaknesses": "The substantive contributions of this paper significantly overlap with previous work on ANN transformer sparsification, demonstrating limited novelty. It essentially replicates the success of existing ANN transformer sparsification approaches, with even the narrative framework bearing striking resemblances.\n\n1. The spatial token scorer is highly similar to the ANN token-pruning one [1]. Thus, the novelty of this paper is significantly challenged.\n\n2. The ablation study in Table 6 is conducted on ImageNet, a static dataset with no temporal disparity, thus I cannot confirm the effectiveness of the temporal pruning scorer. The visualization in Figure 5 does not entirely prove the effectiveness of the temporal pruning scorer. Instead, it exhibits limitations in spatial perception of the spatial scorer. \n\n3. It is very counterintuitive that the temporal scorer is used to prune spatial token rather than prune temporal token. Moreover, temporal pruning is applied regardless of whether the dataset is dynamic or static.\n\n4. The retaining is already existent. This is also called token emerger technique. Or, retaining can be seen as a naive token emerger that can restore non-informative tokens for future use. I can easily give an example of such retaining [2].\n\nIn short, this paper shows severe duplication of ANN token-pruning domain. In essense, the success of this paper's method is inherited from the ANN  token-pruning  works. Such that this paper's contribution is limited. Moreover, the spatial token pruning may be even better if the most advanced ANN token pruning is applied.\n\nThis is more like a very good technique report concerning the verification of ANN methods applied to SNNs.\n\n[1] Similarity-Aware Token Pruning: Your VLM but Faster\n\n[2] HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KF6kfA1mVV", "reviewer": "Reviewer_r4rD", "summary": "This paper introduces TP-Spikformer, a simple and effective token pruning method designed to reduce the high computational and storage costs of large-scale spiking transformers, making them more suitable for resource-constrained devices. The core of the method consists of two main contributions. First, it proposes a heuristic Information-Retaining Token Pruning (IRToP) criterion, which scores and identifies important tokens by evaluating both their spatial distinctiveness from neighbors and their temporal variation across time steps . Second, it introduces the Information-Retaining Architecture (IR-Arc), which, instead of permanently dropping unimportant tokens, applies a block-level early stopping strategy—uninformative tokens bypass computation within a block and are then reassembled with the processed informative tokens. A significant advantage of this approach is its versatility and efficiency, as it requires no retraining or architectural modifications, achieving competitive performance even with zero fine-tuning. The authors demonstrate the method's effectiveness, efficiency, and scalability across diverse architectures (like Spikformer, QKFormer, and SDT) and a range of visual tasks, including classification, detection, segmentation, and tracking.", "questions": "1. Can the proposed method be applied to train a spiking transformer from scratch?\n2. How could the proposed method be generalized to non-grid-like modalities?\n3. How well does the proposed method preserve key information during pruning?What are the key differences between token pruning in spiking transformers and conventional ANN-based transformers?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper presents a robust and highly impactful contribution, demonstrating exceptional strengths across originality, quality, clarity, and significance. The work's originality is outstanding, introducing a novel, training-free token pruning framework for spiking transformers, which stands in stark contrast to prior methods that require costly retraining and architectural modifications. The core ideas are creative and well-motivated: the bio-inspired IRToP criterion offers a new heuristic for identifying important tokens based on both spatial saliency and temporal dynamics, while the IR-Arc architecture cleverly uses a block-level early stopping and reassembly strategy instead of direct token removal, making it compatible with modern hierarchical SNNs. The research quality is superb, validated through extensive experiments across a diverse set of architectures (Spikformer, SDT-V1/V3, QKFormer) and a wide range of tasks, including classification, detection, segmentation, and tracking. The results consistently show significant reductions in computational overhead with minimal performance degradation, and thorough ablation studies rigorously justify the design choices. The paper is presented with excellent clarity, logically progressing from a well-defined problem to an elegant solution, with effective visualizations and a clear algorithm to aid understanding. Finally, the work is of high significance as it provides a simple, practical, and broadly applicable tool that addresses a critical bottleneck in deploying large-scale SNNs. By enabling efficient compression without retraining, TP-Spikformer dramatically lowers the barrier for applying spiking transformers in resource-constrained, real-world scenarios, marking a key step forward for the field of energy-efficient neuromorphic computing.", "weaknesses": "1. The paper would be strengthened by providing further experimental results, such as from an entropy or visualization perspective, to more rigorously justify the effectiveness of the Spatial and Temporal token scorers in measuring token importance.\n2. The authors state that TP-Spikformer performs well even without training, but its accuracy drops significantly when using QKFormer and SDT-V3.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Z9m1HPTMe7", "reviewer": "Reviewer_G33Y", "summary": "This paper proposed an information retaining token pruning framework for spiking transformers.", "questions": "see above", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper proposed an information retaining token pruning framework for spiking transformers. \n\n2. The writing in this paper is good.\n\n3. The experiments are quite extensive, including classification, segmentation, detection, and tracking tasks.", "weaknesses": "1. My main concern lies in the motivation. Currently, directly trained spiking Transformers are relatively small- or medium-scale models. Although pruning slightly reduces performance while improving throughput（eg, TP-Spikformer with SDT-V1-8-768 on imagenet: -1.53% acc, thr 29%）， this trade-off does not constitute a strong motivation.\n\n2、There is a lack of discussion on overall model training costs, such as training time and memory consumption.\n\n3、There is a lack of spike-driven characteristics and energy consumption discussion on the heuristic spatiotemporal information-retaining criterion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4WLKSPClx4", "reviewer": "Reviewer_BtDR", "summary": "This paper presents TP-Spikformer, a method to speed up Spiking Transformers by pruning unimportant tokens. Its main ideas are: 1) a new scoring rule called IRToP, which finds important tokens by checking if they look different from their neighbors (space) and if they change over time (time); and 2) a pruning architecture called IR-Arc, which makes unimportant tokens \"skip\" the heavy computation in a block instead of deleting them. This keeps the model's structure intact. Experiments show the method works well on many models and tasks, and impressively, it boosts speed with almost no accuracy loss even without any retraining.", "questions": "1. How long does the grid search for finding the pruning ratios actually take? Have you thought about a simpler or automatic way to find these numbers?\n2. Could you explain why you chose to skip tokens instead of merging them?\n3. Do you think your method would work for SNNs that are converted from ANNs? For example, you can apply your method to \"Masked Spiking Transformer\".", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The method is very versatile. It works on simple Spiking Transformers, complex ones with convolutions (like QKFormer), and on many different tasks like classification and detection. This shows it's a useful, general tool.\n2. The biggest plus is its \"training-free\" ability. This is very practical for real-world use where retraining is too expensive.\n3. The paper backs up its claims with strong experiments. The ablation studies clearly show why each part of the design is necessary, and the results on big datasets like ImageNet prove that the speedup is real.", "weaknesses": "1. The paper doesn't have a good, automatic way to decide how many tokens to prune in each layer. For some models, they just set the numbers by hand. For others, they run a slow and expensive \"grid search\" beforehand. The reported speedup doesn't include this search time, which makes the method seem faster than it is to set up.\n2. The method prunes at the whole \"block\" level. A token either skips both the Attention and MLP parts, or does both. It might be better to prune them separately (e.g., prune more tokens for Attention and fewer for MLP) to get a better speed-vs-accuracy balance.\n3. The paper only considers skipping tokens. Another popular technique is \"token merging,\" where several similar tokens are combined into one. This might preserve more information than just letting old tokens pass through unchanged. The paper should have compared its method to token merging.\n4. All experiments were on \"directly trained\" SNNs. It's unclear if the method would work on ANN-to-SNN conversion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)