SMixer: Rethinking Efficient-Training and Event-Driven SNNs
Authors: Yijie Lu
Xinhao Luo
Yixing Zhang
Zhiyan Wang
Wentao Li
YanhanWang
Zhi Liu
Zhaokun Zhou
Guoqi Li
Venue: ICLR 2026 Poster (2026)
Keywords: Spiking Neural Network
Efficient Computation
Prune Method
Primary area: applications to neuroscience & cognitive science

Abstract: Spiking Neural Networks (SNNs) offer a promising, energy-efficient paradigm for computation, but their practical application is hindered by challenges in architecture design and training costs. For example, Spiking ResNet exhibits relatively low performance, whereas high-performance Spiking Transformers are not truly event driven and cannot be implemented on asynchronous chips. Moreover, the intrinsic time steps and neuron state dynamics result in a substantial computational overhead for training SNNs on GPUs. In response to these problems, we discuss rational architectural design for SNNs and argue that such designs should exhibit three key characteristics: operations fully supported by asynchronous scenarios, low training overhead and competitive performance. In light of this, we adopt the event-driven friendly Spiking Mixer (SMixer) as the foundational architecture and develop a spike feature Spatial-Temporal Pruning (STP) framework with a high pruning ratio and no trainable parameters to reduce the training overhead. Based on a statistical analysis of sparse spike features, STP eliminates redundant spike features across both spatial and temporal dimensions, thereby reducing the input features and computational load during training. It adaptively selects the most salient spike tokens spatially and dynamically constrains neuron firing rates temporally. By leveraging STP and architectural adaptation, SMixer accelerates training while ensuring a fully event-driven characteristics and maintaining competitive performance, offering valuable insights for the design of efficient, event-driven SNNs.

Published as a conference paper at ICLR 2026
SMIXER: RETHINKING EFFICIENT- AND EVENT-DRIVEN SNNS
Yijie Lu
XXinhao Luo
Yixing Zhang
'Zhiyan Wang Wentao Li Yanhan Wang
ZZh Liu
3/Zhaokun Zhou'
Guoqi Li
Peking University
Institute of Automation, Chinese Academy of Sciences
3 Shandong University
Tencent
2 Chaoknzzhou@tencere com,
guoqi 11@ia.ac. ac.cn
ABSTRACT
Spiking Neural Networks (SNNs) offer promising, energy-efficient paradigm for computation, but face challenges in performance and training costs. For example, Spiking ResNet exhibits relatively low performance, whereas high-performance Spiking Transformers are not truly event driven and cannot be implemented on asynchronous chips. Moreover, the intrinsic time steps and neuron state dynam- ics result in substantial computational overhead for training SNNs. In response to these problems, we discuss rational architectural design for SNNs and argue that such designs should exhibit three key characteristics: fully event-driven op- erations, low training overhead and competitive performance. In light this, we adopt the event-driven friendly Spiking-toke token Mixer (SMixer) as the foundational architecture and develop a spiking-featur Spatial-Tempoaa Pruning (STP) frame- work with high pruning ratio and trainable parameters reduce the training overhead. Based on statistical analysis of sparse spiking features, STP eliminates redundant spiking features across both spatial and temporal dimensions, thereby reducing the input features and computational overhead during training. Specifi- cally, it adaptively selects the most salient spike events in the spatial domain and dynamically constrains the neurons' simulation time steps and firing thresholds in the temporal domain. By leveraging architectural design and STP, SMixer accel- erates training while ensuring fully event-driven characteristics and maintaining competitive performance, offering valuable insights for SNNs' design.
INTRODUCTION
Spiking Neural Networks (SNNs) Maass 1997). the third generation of neural networks, are distin- guished by their biological plausibility Roy et al. (2019), event-driven nature, and energy efficiency. By emulating the dynamics of biological neurons, SNNs employ asynchronous binary spikes to transmit information. Consequently, a neuron's 's membrane potential updated only upon spike arrival. This event-driven property allows SNNs to inherently avoid computations involving zero- valued activations, making them highly suitable for implementation on specialized neuromorphic hardware like TrueNorth Merolla
MeuollrthMMerolla et al. (2014) and Loihi Davies et al. (2018).
Despite the encouraging progress of SNNs, several challenges hinder their application in real-workl pattern recognition. A fundamental difficulty lies in developing potent architectures for event-driven SNNs that not only achieve competitive performance but also adhere to the principles of purely event-driven operations for practical deployment. While Spiking Convolutional Neural Networks Lv
al. 2023) often lack sufficient performance, Spiking Transformers Zhou et al. (202cc 2024a); Yao
al. 2025 have achieved the of-t--oftthearr (SOTA) results across various domains. However,
critical issue arises on asynchronous hardware where spike arrival times are less precise than in synchronous, clock-driven scenarios. The core Spiking Self-Attention mechanism, which relies on the multiplication of two spike matrices, is susceptible to significant computational deviations under such conditions Deng et al. (2024). SNNs' high training cost is also a critical issue that cannot be overlooked, primarily due to two reasons: i) Before deployment on neuromorphic chips, spiking
"Corresponding authors.

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Published as a conference paper at ICLR 2026
neural networks typically require training Graphies Processing Units (GPUs) Fang et al. 2023 SNNs cannot be trained on GPUs in a truly event-driven manner, which means that even zero- valued features consume computational resources. ii) The inherent time steps and hidden states of spiking neurons further occupy computational resources. To address these challenges, we propose that rational architectural design for SNNs should embody three fundamental characteristics: fully event-driven operations, low training overhead and competitive performance.
Our purpose is to explore potential SNNs that simultaneously satisfy the above characteristics. Com- pared to Spiking Transformers, Spiking Token Mixer Deng et al. (2024) emerges as more promis- ing and feasible -rent-driven architecture that the spike-based matrix multiplication used to obtain attention weights is replaced with learnable parameters. Subsequently, we need to investigate ap- proaches to reduce the training overhead of SMixer. The intrinsically low firing rates of SNNs result in significant network redundancy, making pruning a natural and promising approach for network lightweighting and acceleration. However, pruning SNNs presents several key challenges compared to ANNs: First, common unstructured weight pruning in SNNs usually offers limited practical accel- eration and incurs additional training overhead, rendering its extension to SMixer impractical. This motivates the structured feature pruning for acceleration. Second, achieving significant acceleration necessitates a much higher pruming ratio (2 0.3) in SNNs than that in ANNs. Addressing these challenges, we begin with analysis of the SMixer's spiking feature, Spatial-Temporal Spiking Feature Redundancy. Spiking representations are concentrated in specific spatial-tempont regions, implying that a large number of spike tokens containing lowinfformation can be pruned, which lays the foundation for high-ratio pruning. Furthermore, lightweight pruning strategy is needed to avoid introducing excessive gruning-related computational overhead.
We explore the potential of employing Spiking-token Mixer as a prototype within mainstream archi- tectures and find that SMixer achieves competitive performance compared to spiking self-attention mechanisms across various architectures. To further reduce the training overhead, we propose a Dynamic Spatial-Temporal Spiking Pruning (DSTSP) framework tailored for the SMixer, which di- rectly prunes redundant spiking feature to accelerate training while maintaining performance at high pruning ratio. Inspired by previous studies measuring semantic information through activation values in feature maps Zagoruyko & Komodakis (2016); Ding et al. (002)), spiking feature redun- dancy is determined by accumulating spike event counts within specific regions, such as within a single token a particular time step. Features with high accumulated values are considered impor- tant, whereas those with low values are regarded as redundant. The Spiking Spatial Token Pruning scores tokens by summing their spike values and ranking them adaptively based network output. Mixer encoder, while low-scoring lones are
High-scoring tokens are passed to the Spiking- discarded or merged. For the temporal dimension, Dynamic Spiking Temporal Pruning dynamically reduces both the upper bound on the total number of spikes that neurons can emit and the number of time steps, thereby decreasing the latency of SMixer. By integrating the Mixer into various architec- tures, SMixer achieves superior performance compared to the original frameworks, demonstrating the potential as an mainstream SNN structure. We summarize the contributions as follows:
We analyze the inherent architectural requirements for event-driven SNNs and the need for effi cient training methodologies. Based this, we propose blueprint for high-ratio spike feature structured pruning framework built upon the Spiking-token Mixer.
Based on Spatial-Temporee Redundancy in Spiking-token Mixer, we develop the Dynamic Spatial- Temporal Spike Pruning framework, which integrates dynamic spatial and temporal spike feature pruming methods.
We demonstrate that the SMixer architecture can achieve performance comparable to that of the Spikformers. Furthermore, we show that our efficient pruning framework built upon SMixer, accelerates training while maintaining performance close to the original model across various neuromorphic and static datasets.
2
RELATED WORK
2.1 DEEP SPIKING NEURAL NETWORKS
Recent advancements in Artificial Neural Networks (ANNs) have enabled significant performance improvements in SNNs, primarily through the adaptation of cutting-edge architectures from the
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ANN domain. SpikingCNNs Lv et al. (2023) mix spike-form tokens with the learned weights of convolution kernels. However, its performance remains suboptimal. Spiking Transformers Zhou iet al. (2023c a): Zhang et al. (2024): Shi al. (2024) Yao et al. 2023a); Qiu et al. (2024); Wang et al. 2025): ) Qiu et al. (202 Zhou et al. (2024a) Yao et al. (2025 mix tokens with pairwise corre- lations weight matrix between spike-form query and key tokens and re-weights the spike-form value
vectors to synthesize new token representations, which arrives the SOTA in various tasks. However, performing matrix multiplication between two spike matrices during the forward pass incompati- ble with asynchronous neuromorphic hardware. In event event-driven regimes, the spike arrival times lack the temporal precision, which results significant differences in the output, ultimately precipitating marked degradation in performance Deng et al. )0224. By jointly optimizing hardware feasibility and accuracy, STMixer Deng et al. (2024) fuses the spiking token feature with trainable attention weight map, thereby eliminating spike- spike-matrix multiplication. This design choice renders STMixer a significantly more practical and scalable architecture for real-world neuromorphic deployment.
2.2 SNN PRUNING METHODS.
The pruning methods aim to further enhance the efficiency of SNNs. Neuro-inspired strategies fo- cus on synaptic regeneration Kundu et al. (02II) and dendritic motion Kappel et al. (2015). Qi al. Qi al. (008)) designed connection gates for synaptic pruning during the training process. Kim et al. Kim et al. (2022) focus on lottery ticket-based methods, integrating Iterative Magni- tude Pruning Frankle & Carbin 2018) with Early-Bird tickets You et al. (2020) identify more compact SNNS while Deng et al. Deng et al. (2023) formulate connection pruning and weight quan- tization as unified constrained optimization problem, which they solve by using Spatio-tenn neaporl backpropagation and the alternating direction method of multipliers. Grad R &Chen et al. (2021b) makes improvements to Deep method by introducing weight regeneration mechanism. RCMO- SNN Chen et al. (2023) presents end-to-end minimax optimization approach for sparse learning. For Spikformer Zhou et al. (202cc), Zhou et al. Zhou et al. (2025) propose Opatial-Tempont Spik- ing Feature Pruming, and Liu et al. Liu et al.) (024) develop SparseSpikformer. Different from the above, we seek to devise pruning methods tailored to superior Spiking-toke Mixer architectures, which is direction that, to the best of our knowledge, remains entirely unexplored.
3 METHOD
3.1
SPIKING-TOKER MIXER
The Spiking Self-Attentin (SSA) involves synchronized matrix operations between spike-based queries Q keys K, and values V, whereas the Spiking-token Mixer involves asynchronous compu- tations between weight matrices and inputs, as detailed below,
Q = =SN(LBBNg(X)) K SN(L-BN (X)), V -N((-BBN(()))
(1)
SSA(Q. V) SN<QKTV s),
(2)
SMxier (X) = SN (WN()X)
(3)
where SA denotes the spiking neuron and L-BN represents that the features pass through Linear and Batch Normalization sequentially. 8 is the scaling factor. X is the spike-form input. However, calcu- lating multiplications between spike-form Q and K is unsuitable for asynchronous hardware. In com-
Encoder Block x
Spiking Patch Spliting
Spiking Linear
Spiking Linear
Spiking MLP
CH
parison, Spiking-token Mixing can serve as a sub-
stitute for the SSA. Q and K converge into single learnable matrix WM for attention weight, which is to gradually fit the attention map during the train-
SMixer W eigght
Figure 1: The overview of Spiking-token Mixer.
ing process. In Figure [ the overall architecture includes Spiking Patch Splitting (SPS), several en-
coder blocks composed of a Spiking Spiking-token token Mixer module and Spiking Multilayer Perceptron (MLP), and classification head (CH).
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3.2 PPATIAL-TEMPORA, SPIKING FEATURE REDUNDANCY SMIXER
To enable efficient feature pruning in SMixer, we begin by evaluating the significance spiking features using the Spike Intensity Value (SIV), which is defined the sum of spike events within designated feature region.
Similar to the concept of activa- tion magnitudes in ANNs Zagoruyko & Komodakis 2016); Ding al. (2023), a higher SIV indicates a greater concentration of semantic in- formation. Our analysis of the DVS- Gesture reveals that the SIV distribu- tion is highly imbalanced, with the majority tokens exhibiting low val- ues, as shown in Figure [] This imbalance closely linked to model performance: model that uses only high-SIV tokens demonstrates con- siderably better accuracy than one lying on low-SIV tokens (97 97% vs 79.9%). Visual evidence further sup- ports this, showing that regions with high SIV align with foreground objects, while low-SIV regions tend correspond the background, as shown Figure 2 It shows samples from DVS-Gesture with spatial pruning ratio of 0.3 and temporal pruning ratio of 0.5 The left and middle columns of each sub-figure display the original image and its corresponding original features. The right column demonstrates the result obtained by retaining feature tokens with high SIV. These findings suggest that critical semantic content predominantly contained within small subset of high-SIV tokens. Importantly, the computation of SIV is highly efficient, involving only a simple summation that is well-suited to the operational characteristics SNNs.
Image
Feature
Pruned
Image
Feature
Pruned
Figure 2: Visualization of Spatial Temporal Spiking Feature Redundancy.
1.1-
RIE
MS-SSIM
1.0
Tolon Percentage H
0.9
0.8
0.7
0.6
0.5
Spiko Intensity Value
HP
ANN-LP
Figure 4: The strategies investigated include pruning tokens with low-SIV (LPP), high-SIV (HP), and random selection (RP). ANN-LP de- notes the performance of the LP strategy the ANN- TrN-Tranffrrmr.. The Y-axis denotes the metric values.
Figure 3: Distribution of Spike Intensity Value DVS-Gesture. The maximum and minimum values are 125 and O. The dashed line is the boundary for 50% of Spiking-tokenss
To validate the efficacy of our Spiking Information Value (SIV) metric quantifying feature re- dundancy, we conducted an analysis on CIFAR-100 using RIE and MS-SSIM scores (details in Appendix C.. As shown in Figure 4 we evaluated several token pruning strategies: low-SIV (LP), high-SIV (HP), and random pruning (RP), comparing pruned feature outputs against the unpruned (ANN-
baseline. Our analysis, which also included the LP strategy on standard ANN-Transfome LP), revealed two key findings. First, SIV is substantially more effective at exposing redundancy in SNNs than ANNs. Second, the SIV-informed LP strategy the most effective approach for SNNs. This core insight serves the foundational principle for DSTSP framework.

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11
Lay
Layer
Activattd SMbberr Weight
(a) Dynamic Spiking Spatial Pruning
(b) Dynamic Spiking Temporal Pruning
Figure 5: The illustration of Dynamic Spatial- Temporal Spiking Pruning.
3.3 DYNAMIC SPATIAL-TENTE TEMPORAL SPIKING PRUNING
Inspired by the high sparsity and redundancy of spiking features, we propose a lightweight method, the Dynamic with low SIV, as illustrated in Figure 5 This approach aims to substantially reduce training costs while preserving inference performance. Unlike conventional pruning in ANNs, which often relies on complex, trainable modules, our method is specifically designed for SNNs. It avoids introducing any learnable parameters by leveraging simple, addition-based operations that align with the spike- driven characteristic of SNNs. DSTSP operates sequentially across two dimensions. First, in the temporal domain, it computes the SIV for each spiking feature map per time step, retaining only specified percentage of the highest-scoring maps. Subsequently. in the spatial domain, it evaluates the SIV of individual spiking tokens, preserving only those with high activation values. The specifics of DSTSP are detailed in the following sections.
Spiking Pruning (DSTSP), to prune significant fraction of features
Dynamic Spiking Spatial Pruning (DSSP) analyzes the SIV of each token to determine its seman- tic information and prunes tokens with low SIV. For spike-form feature X E g7'xNxC' where T represents the time steps, N and C' represent the number of spiking tokens and channel dimensions, respectively. The DSSP is conducted as follows,
Is = Σ X = SIRT(Is, X), = N Ps), = X|NN
(4)
SMixer (X) = SN (W') W M = S'
(5)
where X is sorted in descending order to obtain X, according to the spatial SIV Is. N' is the number of spiking tokens after pruning and Ps denotes the spatial pruning ratio. X' is the remained feature after DSSP. W M denotes the activated attention weight, for which we explore two approaches. The first approach treats as a static N' x N' matrix. The second approach involves randomly selecting N' shared row and column indices during training and reverting to full-sized matrix for inference. Both strategies yield nearly identical performance at inference time.
Dynamic Spiking Temporal Pruning (DSTP) prunes temporal information from two aspects. On one hand, assesses the importance of each time step to remove those that are less significant. Given an input spiking feature X NTxXNX the formulation is as follows:
N
C
I
x F 1], X; SORT(I,, X),
(6)
T'= (1- X |T = SN(X')
Here, IT is the SIV calculated for each time step. Based on these scores, X; represents the feature maps sorted in descending order of importance. The number of time steps retained, 7" calculated based on a pruning ratio Pr. Finally, X' is the temporal feature after the pruning. Reducing the number time steps for feature inputs into the spiking neurons and network layers directly reduces the number of neuron states and computational cost, thereby lowering training overhead.
Computational Overhead Analysis. For clarity, we choose typical size Spiking-toker Mixer mod- els to compare these operations on the ImageNet, as shown in Table [ The results show that DSTSP significantly lowers the computational overhead. Theoretical analysis is provided the Appendix
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Table I: Comparison of the Increass: and Reducee. SMixer- denotes for Spiking-token Mixer
with L layers and D channels. The Rate represents the ratio of ReReucee to original cost OP Origin
Model
OP Increasee (M) f OP RRdduce (M) 4
OPorigin
Rate(%)
2ahxxer-2-256
0.33
243
1106
21.97
SMixer-4-384
0.45
1060
4867
21.78
SMixer-8-512
0.58
3741
17073
21.91
Image
Feature
DSTSP
Image
Feature
DSTSP
Figure 6: Visualization of SMixer and relating DSTSP on the ImageNet- IK. Feature and DSTSP represent the attention maps generated by the original SMixer module before and after being pruned by our DSTSP method, respectively
EXPERIMENTS
In this section, we validate DSTSP using Spiking Token Mixer framework as the baseline. We first conduct experiments classification tasks, including CIFARIO-DVS al. (017). DVS128 Ges- ture Amir et al.) (2017), CIFARIO, CIFARIOG 10A0000 ( (2009), and ImageNet Deng et al. (209)). These datasets cover both neuromorphic and conventional vision domains. Our evaluation centers on training time, energy cost, GPU memory and inference accuracy. We further assess DSTSP on the more demanding task of time series and object detection. Final, the ablation study examines the efficacy, certain strategies, and position of DSTSP. The training epochs, hyperparameter settings are consistent with the original models. Further details are provided in Appendix
4.1 RESULTS ON IMAGE CLASSIFICATION
ImageNet. We evaluate the feasibility of Spiking-token Mixer (SMixer) paradigm Deng et al. 2024) and DSTSP on the ImageNet-IK IK dataset. We first analyze the effectiveness of DSTSP using the original STMixer-8-511 and STMMxxr--87768 as baselines. Besides, we further replace the Spiking Self-Attention in three representative state-of the-an art Spiking Transformer frameworks (SpikformerV2 Zhou et al. (2024b), QKFormer Zhou et al.) (0024)). and Spike-driven Transformer- V3 Yao et al. (2025) with Spiking-token Mixer and further assess DSTSP pruning framework, supported by visual analysis, to demonstrate its efficacy. As detailed in Table replacing SSA with Spiking-toker Mixer yields comparable accuracy the original frameworks similar parameters, demonstrating its capability to replace the Spiking Transformer and serve as new prototype. With a fixed spatial pruning ratio of 0.30 and a pruned time step of our DSTSP shows remarkable effi ciency gains. Notably, on the STMixer-8-766 model, our pruning method improves accuracy by 0.2 % over the baseline. This high performance are achieved while reducing GPU memory consump- tion to 76.44% and energy to just 03.03% of the original, alongside high increase in throughput. When applied to the modified Spikformer- V2 (T=4), our pruming framework results in only a minor 1.3% accuracy loss while also delivering substantial computational savings. Variants based QK- Former and SDT-v3 also diminish energy consumption and shorten training time while incurring a performance drop of less than 2%. Our qualitative analysis in Figure 6 offers further validation.

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Published as a conference paper at ICLR 2026
Table Performance of DSTSP on ImageNet. Power is calculated as the average theoretical energy consumption when predicting image from ImageNet test set. The power data for ours is evalu- ated according Appendix and the power data of other works are obtained from related papers. "Model-L-D" denotes the specific Model with L encoder blocks and D channels. "Model M indicates the conversion of the original Spikformers Model into the Spiking-token Mixer. We set spatial pruning ratio to 0.3 and perform temporal pruning to single time step.
Methods
Architecture
Time Step
Param (M)
TP (imis)
Memory (MB)
Power (md)
Top-1 Ace (%)
STMixer
STMixer-8-768
STMixer-8-512
61.16
30.12
211
11762
2.20
76.68
73.82
STMixer-8-512
27.61
250
9578
1.68
73.56
STMixer DSTSP
STMixer-8-768
58.66
162
13002
2.36
76.87
Spikformer
SpikformerV2
Spikformer V2-8-512
Spiktormer V2-8.384
29.11
51.55
113
82
12170
6880
4.69
2.84
79.05
78.80
Spikformer
18786
Spikformer V2-8-384
27.97
156
3490
1.13
76.39
Spiicfrrmrr22
Spikformer V2-8-512
Spikformer V2-8-384
27.97
48.56
134
65
5384
9156
3.65
2.12
79.12
79.16
Spikformer V2-8-512
56
12982
7.97
Spikformer V2-8-384
27.34
2455
2584
0.83
76.22
Splicformer22 V2 DSTSP
Spikformer
Spikformer V2-8-512
47.93
198
3368
1.98
78.99
Spikformer V2-8-512
47.93
98
10387
3.44
79.15
Former
HST-JO-384
16.47
377
13841
3.38
75.52
OKFormer + M
HST-10-384
18.31
365
8385
2.76
76.03
HST-10-512
29,70
325
13051
4.06
78.69
QKFormer DSTSP
HST-10-384
14,92
HST-10-512
25.92
398
9751
3.15
77.39
Aallient-mmnnsfommee
5.1
300
9040
1.70
75.30
SDT-V3
ffllcient-transfoomss
Efficient trunsformer-L
19.00
197
16318
5.90
79.80
Efficient transformer-S
6.37
373
7396
1.42
75,25
SDT-V3
3ffcieetttmunssoomsss Amunsformer-MM
10.15
250
9988
2.56
78.65
19.36
219
13912
4.93
79.25
Iilecienttranssoumss
5.05
398
6060
1.01
74.15
SDT-V3 M DSTSP
Efficient transformer M
9.42
338
8210
2.02
76.63
tunnsoormer..
Table 3: Comparision on CIFARIO, CIFARIO0, DVS128, CIFARIO-DVS. "Param" denotes "Pa-
rameter (M)", "Ace" denotes "Top-1 Accuracy (%)", "T" denotes "Time Step".
CIFARIO
CIFARI00
DVS128
CIFARIO-DVS
Method
Parum
T
Acc
Param
T
Acc
Param
T
Acc
Param
T
Acc
Spik Spikformer Zhou et al. 2023c
9.32
4
95.51 51
9.32
78.21
2.57
16
98.30
2.57
16
80.90
Spikingformer Zhiu et CMI. /hou S-Transforme Yão et 2023a
2023a
9.32
9.32
78.21
2.57
16
98.30
2.57
81,30
2023b
2.57
98.60
2.57
10.28
4
95.60
10.28
78.40
2.57
16
99,30
2.57
16
80.00
SMixer
8.29
95.49
8.29
80.00
2.63
10
97.91
2.63
10
83,30
8.29
96.01
8.29
81.87
2.63
16
98.61
2.63
16
83.02
SMixer<DSTSP
8.24
8.24
79.71
2.55
97.56
2,55
82.56
8.24
4
95.67
8.24
81.03
2.55
16
98.26
2,55
16
82.34
The attention maps from Spiking-token Mixer (Feature) effectively highlight key object regions in manner comparable to the original Spiking Self-Attention. Crucially, we observe that after pruning, the remaining tokens consistently align with the most semantically significant parts of the image (DSTSP). This demonstrates our framework's ability preserve essential information.
Cifar and Neuromorphic Datasets. We then conduct experiments on both static datasets (CIFAR- 10, CIFAR-I00 Krizhevsky/ (2009) and neuromorphic datasets (CIFAR I0-DVS Li et al. (2017). DVS128-Gestume Amir et al. (2017)), benchmarking our method against the SMixer baseline as shown in Table 3 By applying fixed spatial pruning ratio of 0.30 and prune the time-step to 1, we observe a mere 0.5% performance drop on CIFAR-10 10 and CIFAR- -000. On the neuromorphic datasets CIFAR10 10-
10-DDSS DVS and DVS128, method incurs performance drop less than 1%.

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Table 4: Experimental results of time-series forecasting on benchmarks with various prediction lengths 6, 24, 48, 96. The best results in SNNs are formatted in bold font format. (L) indicates the higher (lower) the better.
Model
SNN
Metric
Metr-la
Poms-by
Solar
Electricity
24
24
48
24
46
24
48
Transormer
x
RSE.
R*1
.127
.551
.704
.544
A13
.008
895
224
.785
.502
.734
.588
.688
.600
618
.673
.953
.223
.588
.377
.139
.504
.718
.545
.978
.200
.975
.277
.972
.347
.964
.425
Spikfomeer
RSE3
R=1
.881
.753
A91
878
917
242
.21
6017
684
613
627
.399
819
430
7715
-488
.002
656
.371
8555
3775
.386
3953
A50
3443
SMixer
RSEJ
R"1
.734
.544
.519
.730
.422
.802
895
278
.788
.96
.736
.666
.689
.M33
.744
.615
9425
.216
369
.371
.471
.789
.531
.733
.31
985
.971
.305
.967
.322
952
.389
SMixer DSTSP
R"1
.697
-530
.000
277
.75
.712
.62
662
.300
855
.761
.62
.977
.955
.950
995
RSE]
.381
.722
816
.896
.503
.577
.077
.396
.72
.381
.40
.96
.369
.355
.395
.300
4.2 RESULTS ON TIME SERIES TASK
We evaluate the effectiveness of DSTSP on time-series tasks by conducting experiments on four benchmarks Metr-La, Solar, PEMS-Bay and Electricity under prediction horizons of 6, 24, 48, and 96 steps. As shown in T the SMixer paradigm consistently outperforms Spikformer, demon- strating its potential. Besides, integrating DSTSP incurs no significant degradation performance.
4.3 RESULTS ON OBJECT DETECTION TASK
To evaluate the effectiveness of DSTSP on demanding object detection tasks, we first adapt the SpikeYOLO YOLO Luo et al.) (2024) framework by integrating SMixer blocks, then implement DSTSP. Specifi- cally, we replace the convolutional layers in the third and fourth stages of Spike OLO with 4 and 2 layers of SMixer blocks, respectively. On the challenging MS COCO dataset [in et al.] (2014), our model trained with DSTSP by 20% spa- tial pruning rate and single pruned time- step and achieved mAP50 of 57.4, com- pared to the bascline model's 58.9. We consider this modest decrease in accuracy acceptable, given the highly detailed and complex nature of the COCO task. Further deployment details are provided in the Appendix E.3
Table 5: Performance of detection on COCO
val2017 [in et al. (2014). We benchmark our method against representative baselines of both ANN-to-SNN conversion and direct SNN training paradigms.
Methods
Archilectue
Param (M)
Power (m))
Step
MAP4550/5)
Spiking Volo (95)
10.3
ANN2SNN
Spike Callbration (96)
17.1
Frat-SNN [15)
25.1
46.4
Spiking 1971
training
(98)
26.9
Mau. Soiie (28)
75.0
51.2
training
ESpiichormar
04.5
38.7
Dirrct training
SMiser
SMiter DSTSP
39.2
49.7
212
57.4
58.9
4.4 ABLATION STUDY
Pruning Method Ablation. This section details the implementation of various eemporal-spatiin pruning configurations on the CIFAR-10 and CIFAR-100 datasets. We evaluate their effects on key metrics, including energy consumption, FLOOPs, inference latency, and overall performance. Our findings demonstrate that even at substantial pruning ratios, the proposed strategy preserves model robustness while significantly reducing FLOPs and increasing processing speed. Furthermore, we conduct a granular analysis of the impact of different temporal-spatial pruning ratios. As illustrated in the Appendix [E.5] Figure for the OVS-CIFARII and CIFAR-100 datasets, a notable degradation is observed only at exceptionally high spatial-temporal pruning levels.
Comparison of Spatial Pruning Strategies. To validate the proposed pruning method, a compar- ative analysis is conducted against two baseline strategies: random pruning (RD) and the pruning of high-SIV tokens (HP). To ensure fair comparison, the pruning technique is the sole variable, with all other experimental settings held constant. The results affirm our prior findings from the Spatial Temporal Spiking Feature Redundancy analysis, confirming that removing low-SIV regions the most effective approach for information preservation. As depicted in Figure / our method achieve superior accuracy of 81. .33 the CIFAR100 dataset. In contrast, the HP strategy is the
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Table Performance of STSFP on CIFARIO and CIFARI00. We only displayed the GPU, throughput, and FLOPs for CIFARI00 because the results for CIFARIO are almost the same. We adopt the Softmatch strategy by default to deal with the pruned tokens. TP denotes the throughput.
100
DSTSP
95
85
80
75
70-
65-
P.
TP
GPU
PL.PPs
CIFAR100
CIFAR10
CIFARLO--DVS
0
205
5329M
3.45G
81.78
96.01
0.40
0
337
3498M
2.73G
80.81
95.5 44
Figure 7: Comparison of Pruning Strategies. The default spatial pruning ratio is set to 0.3, and time step pruned to
94.67
0.50
3526M
2.54G
81.25
95.90
0.75
667
3384M
2.08G
81.23
95 89
0.30
0.75
738
2346M
2.04G
81.03
95.67
least performant, with its accuracy declining by 2.92% to 78.11% %, while the RD strategy yield an intermediate result.
Comparison with SNN Weight Pruning. In Table 7 benchmark DSTSP against conventional SNN pruning methods. A direct one-to-one comparison is non-trivial, as DSTSP's methodology di- verges significantly from standard synaptic pruning. Consequently, we evaluate performance based on three key criteria: the pruning cost, the degree of sparsity achieved, and the accuracy degrada- tion relative to the original dense model. By integrating pruning directly into the training loop for efficiency, DSTSP's computational cost is treated as a single, unified stage. Notably, our proposed method incurs the minimal performance loss among all techniques benchmarked.
Position of DSTSP. This section analyzes the optimal placement of the pruning module by con- sidering its temporal and spatial operations. A critical constraint dictates that spatial pruning must be performed subsequent to the SPS module, as its 2D convolutional layers depend on the original feature map dimensions (Height Width) that spatial pruning would otherwise alter. The empir- ical results for different module placements are presented in Table g The findings indicate that applying DSTSP during both the training and inference phases (B) yields the highest performance, though employing it exclusively during training also produces commendable results. We further explore strategies involving pruning only during training (T) or inference (I). The performance of the training-on only approach is comparable to that of using DSTSP in both stages, whereas applying solely during inference results in inferior performance.
Table 7: We define the pruning cost as the ra- N. tio N. where N, is the number of epochs for pruning and N₂ is for training the origi- nal dense model. Since pruming into the standard training process with- out requiring additional epochs, its cost is Furthermore, we note that our reported spar- sity measures the ratio of prumed spiking tokens, whereas in prior works it typically refers to the ratio of zero-valued weights.
Table 8: Position of STSFP. For the 'Stage", B indicates that STSFP is used during both training and inference, T indicates usage during training only, and indicates usage during in- ference only. Regarding placement, IS denotes integrating the TSFP within the SPS, while AS indicates positioning the module after the SPS. For CIFARIO-DVS, P. 0.3 and P. = 0.9. For CIFAR100, P. 0.3 and Pt 0.75.
method integrates
Dataset
Stage
TSFP
SSTP
Acc.
Pruning Melhod
Base Arc. (%)
Ciol Prunel Ace. (9)
Spensits
6:90
B
AS
AS
82.56
Shong
B
IS
AS
82.03
6.46
CrdlR
Chenmra Smts
71.34
51.17
67.31
67.47
696
6:55
CIFARIO-DVS
T
I
AS
AS
AS
AS 80.05
82.45
37.67
91.35
630
71.34
96.11
70.94
6.6
B
AS
AS 81.03
70.31
67.55
6:46
ROMD-ANN (000--50--CCmma
74.71
65.7
70,80
0.85
0.97
CIFARIO0
B
T
AS
IS
AS
AS
80.56
80.95
I
AS
AS
79.74
DSTSPP
7978
6:70

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5 CONCLUSION
We begin by analyzing three major challenges currently faced by deep SNNs: event-driven straints, performance limitations, and training overhead. Specifically, we explore the potential of the Spiking-token Mixer prototype in terms of both performance and event-driven characteristics. By integrating SMixer into mainstream SNN variants, we demonstrate that its performance on par with prior Spiking Transformers, highlighting the potential for high-performance architectures that are fully event-driven. To address the excessive training cost associated with SMixer, we propose Dynamic
Spiking Pruning, which reduces training overhead by pruning redun- dant spiking features while maintaining competitive performance. We hope that SMixer can inspire future research in the development of deep SNNs.
ACKNOWLEDGMENT AND DISCLOSURE OF FUNDING
We sincerely thank Dr. Shikuang Deng for his assistance with this work. This work was supported by CAS Project for Young Scientists in Basic Research (YSBR-116), National Natural Science Foundation of China (62325603, 62236009, U22A20103), Beijing Science and Technology Plan (Z241 10000422401),, the 14th Five- Development Plan (2021 (002120225) Research Grant of Na- tional Education Examinations Authority, China, and Qingdao Municipal Science and Technology Benefit People Demonstration and Guidance Special Project under Grant 21- 2f-1-4-sf-2-nsh.
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A COMPUTATIONAL OVERHEAD ANALYSIS
Although they do not introduce additional training parameters, the inclusion of DSTSP in Spiking- token Mixer incurs some computational overhead. For a spiking feature ST'NXC, the total number of additional operations brought by DSTSP is:
OPDSTSP = (2 - - + ( +T².
where Pt denotes temporal pruning ratio and P. denotes spatial pruning ratio. Here we choose the Bubble Sort when discussing computational overhead, using it as the upper bound for the cost of DSTSP. Specifically, for DTSP, the number operations required for SIV addition TNC. while the number of operations required for sorting is T (including comparisons and swaps). For the DSSP applied to the features Xxp S7xxNxC Tp = T (1 P,) after DTSP, the number of operations required for SIV addition is (1 - Pt) TNC. while the number of operations required for sorting (1 - Pi) 7N2, In addition, the indexing operation on the attention weights requires an additional computational cost of N
The reduced number of operations for which the L-layers network inference overhead after pruning is:
OP Reduce P. P.- T (P,- 10NC + P.-NN2)
Besides, the total number of operations introduced by the original Spiking-token Mixer is:
Priign = - T. (10NC2 + C2
The preceding equations lead the conclusion that for large values of P, and Pt, the Pereduee with our proposed DSTSP is considerably lower than that of the original Spiking-token Mixer OP Origin- Furthermore, the computational overhead introduced by the DSTSP operation, DSTSP negligi- ble. During training, the parameter-free DSTSP does not require training or gradient computation. Additionally, pruned features do not need gradient back pack-propagation, further accelerating the train- ing process.
B THEORETICAL ENERGY CONSUMPTION
According to the general convention of SNNs Panda et al. (2200) Yao et al. (2023b)). we posit that the MAC and AC operations are executed on 45mm hardware (orowitz)) 2014). with energy consumption values of ECMAC 4.6pJJ and ECAC 0.9pJ per operation, respectively, The theoretical Energy Consumption (EC) ANNs can be derived as follows:
ECANN = 4.6pJ X MACs.
(10)
In SNNs, the AC operations can be obtained by multiplying the MAC operations by the firing rate f of input spikes and the simulation time step T',
ACs MACs x f x T.
(11)
The operations of the first layer are MACs to map the Doating-point original image to spike features, while subsequent-layers operations are ACs for modeling sparse spiking features,
EC 4.6pJ x MACs' + 0.9pJ
ACS
(12)
where L denotes the number of linear layers in the models. Note that we ignore the energy of BN, as it can be incorporated into the linear layers during inference.
C THEORETICAL ANALYSIS OF SEMIAL-TEMPORAL SPIKING FEATURE REDUNDANCY
We propose metrics support quantitative analysis in Sec 3.2.
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Ratio of Information Entropy (RIE) is employed to quantify the similarity of information content between two feature sets, with score approaching indicating higher degree resemblance. The RIE is formally defined as the ratio of the information entropy of the pruned feature map IE(Xp) to that the original feature map IE(X). The calculation for information entropy is given by:
x)
Σ 2001
Px= log, Px=i,
(13)
and the RIE is subsequently computed as:
1E(Xp) IE(X)
RIE(Xp, X)
(14)
where Px= represents the probability of a given spike value (0 or 1). In our implementation, this probability term is empirically estimated by measuring the mean spike firing frequency across all samples in the test set.
Multi-Scale Structural Similarity (MS-SSIM) quantifies the structural congruence between two images across multiple scales. An MS AS-M score approaching its maximum value of 1 signifies higher degree of similarity. We employ this metric, in conjunction with feature visualization techniques, to analyze features in both the spatial and temporal domains.
D THE DSTSP ALGORITHM WORKFLOW
The complete algorithmic details of DSTSP are presented below in Algorithm
Algorithm Dynamic Spatial- Temporal Spiking Pruning
Spatial-Temporal
Require: Spiking Feature X E gTx Spatial Pruning Ratio P.. Temporal Pruning Ratio P.
Ensure: Pruned Spiking Feature X, E &TbxNNxC where Tp = T. (1 - P.) and N₂ = N. (1 P.)
1: Dynamic Temporal Spiking Pruning
2: Sum the X for each time step to obtain SIV,
3: Sort SIV, to obtain indices of the top Tp largest correspondences in X
4: Select Xip according to the indices for Dynamic Spiking Spatial Pruming
5: Dynamic Spatial Spiking Pruning
6: for j = 1 to T, do
7: Sum spikes of each token in the X, to obtain SIV,
8: Sort SIV, to obtain indices of the top Np largest correspondences in X,
9: Select X j.s according the indices
10: Select Wsssp according to the indices
end for
E EXPERIMENTAL DETAILS
E. IMAGENET- IK EXPERIMENTAL SETTINGS
ImageNet- 1K dataset Deng et al. 2009) is commonly used for computer vision tasks. It spans 1000 object classes and contains around 1.3 million training images and 50,000 validation images. For periments on the ImageNet dataset, we applied the DSTSP methodology to STMixer and three Spik- formers variants Spikformer QKFormer, and SDTV3 by replacing their original token-mixing operators with the corresponding Spiking-toke token Mixer. We keep all training hyper parameters iden- tical to those reported in their respective papers and conduct training on 8 NVIDIA-4090 GPUs.
E.2 SMALL DATASETS EXPERIMENTAL SETTINGS
All experiments CIFARIO and CIFAR100 conducted four 3090 GPUs. We employ the same training script as SMixer and employed identical data augmentation techniques. Additionally, conduct further experiments neuromorphic datasets to demonstrate the effectiveness of
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the variable M indicates the total number of test samples, C specifies the channel count, and L defines the prediction horizon. The term Y is the average value of " specific notation, Ymm denotes the value at the 1-th future time step for the c-th variable within the m-th sample. Concurrently, Ye.: represents the mean of Y computed over all samples. Ground truth values are represented by the symbols Y and Y.m. These metrics are chosen over alternatives like Mean Squared Error (MSE) or Mean Absolute Error (MAE) due to their enhanced robustness Published as a conference paper at ICLR 2026
DSTSP. CIFARIO-DVS Li al. (2117) is neuromorphic dataset that is obtained from the CIFAR- 10 dataset through a DVS camera. There are 10k images in CIFARIO-DVS, and we split them into 9k training images and Ik test images. We follow STMixer and downsample the image resolution from 128x128 to 48x48. DVS128 Gesture Amir et al. (2017) is a gesture recognition dataset that consists of 11 hand gesture classes performed by 29 individuals under different lighting conditions. Notably, we use the ILIF neurons Luo et al. (2024) in all our experiments.
E.3 COCO EXPERIMENTAL SETTINGS
The prevalent reliance of existing SNN object detection models convolutional networks makes a non-trivial task to create a framework based on a spiking-tokent mixer with DSTSP. Notably, even recent architectures such the Meta Spike-driven Transformer V2 Yao et al. (2024). SpikeY- OLO Luo et al. (2024) are still fundamentally structured around a large number of convolutional layers. To demonstrate the capability of the Spiking-token Mixer and our DSTSP training method- ology for challenging object detection tasks, we introduce a hybrid architecture that circumvents the heavy dependence on convolution. Our approach adapts the advanced Spike YOLO model by integrating Spiking-token Mixer blocks at critical feature extraction stages. Specifically, we replace the convolutions the third and fourth stages of Spike YOLO with layers and 2 layers of Spiking- fix the input resolution 640x640. In the
token Mixer blocks, respectively. During training, inference, though we rescale the image while preserving its original aspect ratio and set the longer side to fixed maximum of 672 pixels, their spatial dimensions remain non-uniform. Before enter- ing each SMixer block, we first convert the feature map to 640x640 via adaptive average pooling; after the block, we bilinearly interpolate the output back to the original resolution. However, such resolution switching still incurs non-negligible accuracy degradation. Thus, future work will aim to develop more advanced feature-pruning algorithms for similarly complex visual tasks.
E.4 TIME SERIES TASK EXPERIMENTAL SETTINGS AND RESULTS
Datasets The key statistics and data distribution for each dataset are summarized in the Table 9
Table 9: The statisties of time-series datasets.
Dataset
Samples
Variables
Observation Length
Valid-Test Ratio
Metr-la 34,272
207
12, (short-term)
(0.7, 0.2, 0.1) (0.7, 0.2, 0.1)
Pems-bay
52,116
325
12, (short-term)
Solar-energy 52,560
137
168. (long-term)
(0.6, 0.2, 0.2) (0.6, 0.2, 0.2)
Electricity 26,304
321
168, (long-term)
Metrices For evaluating time-series forecasting performance, we employ two primary metrics: the coefficient of determination (R²) and the Root Relative Squared Error (RSE).
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Fm 1|2
RSE
(16)
M Ymm
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In the above Formulas 15
to the scale of dataset values, rendering them highly suitable for the time-series forecasting context.
16

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Published as a conference paper at ICLR 2026
22
02
71
37
36
36-
21
0.6
Spattsl Pruniing Rebo
Fruning Retto
Figure 8: Performance of Spatial-Temporal Spiking Feature Pruning on CIFARIO-DVS and CI- FAR100. The temporal pruning ratio is shown in the figure, prune_T means that discard n time steps. The original time step in CIFARIO-DVS is set to 10, and CIFAR100 is 4. The spatial pruning ratio is varied from 0.1 to 0.9.
Model Architecture and Training Hyper- parer-parametersse A fixed setting of 4 time steps is used for all SNNs. We build a model inspired by the working CPG et al. (2025). replacing Spiking Self- Attention with the SMixer architecture, and introduce the DSTSP pruming mechanism before the first SMixer block. All variants are built with blocks. The dimension for features is set to 256, while the hidden feature dimension within the FFN 1024. We configure the training process with batch size of 64 and utilized the Adam optimizer. The learning rate was managed by a cosine scheduler, starting at 1 10-4, 4, To prevent overfitting, early stopping mechanism was implemented with patience of 30 epochs. All experiments are executed on 32G-V100 GPUs
E.5 MORE ABLATION RESULTS ON SENTILL-EMMPRAL PRUNING RATIO
As shown in Figure. we present the performance of our model on the CIFARIO-DVS and CI- 100 datasets under various spatial-temporal pruning ratios. We observe that under high tempo- ral pruning ratio, performance degrades as the spatial pruning ratio increases. This suggests that the model requires sufficient information make accurate inferences. Conversely, with low temporal pruning ratio, we find that a certain degree of spatial pruning can lead to improved performance. For instance, the CIFAR 100 dataset, the best performance is achieved with spatial pruning ratio of 0.4 when half of the time steps are pruned (i.e., two remaining time steps). Furthermore, when both spatial and temporal pruning ratios are moderate, the performance loss remains marginal, which demonstrates the robustness of our proposed pruning method.
F
USE OF LLMS
We declare that the LLMs are used solely to aid or polish the writing and are not involved in the development of the main methodology or comparative experiments.
17


Reviews:
[{"review_id": "DAKulYBNXp", "reviewer": "Reviewer_sSmP", "summary": "The paper proposes SMixer, an event-driven SNN architecture that replaces spiking self-attention with a Spiking-token Mixer to improve hardware feasibility while retaining competitive accuracy. Building on SMixer, the authors introduce Dynamic Spatial-Temporal Spiking Pruning (DSTSP)—a parameter-free framework that exploits sparsity in spike features by (i) pruning low-information tokens in space based on a Spike Intensity Value (SIV) metric and (ii) reducing uninformative time steps and spike counts in time, thereby cutting training overhead without materially degrading performance. Across ImageNet-1K, CIFAR-10/100, DVS datasets, time-series forecasting, and COCO detection, the approach demonstrates substantial reductions in memory/energy and higher throughput, with accuracy maintained close to baselines; in some ImageNet settings it even slightly improves top-1. The work argues for SNN designs that are simultaneously fully event-driven, low-overhead to train, and competitive in performance, positioning SMixer + DSTSP as a practical blueprint for neuromorphic deployment.", "questions": "1.Give more ablation on robustness of SIV.\n2.Does increasing the number of training epochs adequately compensate for the capacity loss induced by pruning?\n3.Please further explain why DSTSP is uniquely suited for the SMixer architecture rather than the SSA architecture.\n4.Provide experimental results on HarDVS dataset.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear problem framing and design criteria. The paper precisely motivates why spiking self-attention is ill-suited to asynchronous chips and formalizes three desiderata—fully event-driven operation, low training overhead, and competitive performance—guiding the architectural choices. \n\n2. Hardware-friendly mixer in place of SSA. Replacing spike-matrix multiplications with a learnable mixer weight (WM) preserves the benefits of token mixing while avoiding timing-sensitive QK interactions, improving deployability on neuromorphic hardware. \n\n3. Parameter-free pruning mechanism. DSTSP is simple (addition/sorting), incurs negligible extra compute, and integrates into training without auxiliary modules or epochs; pruned features also skip back-prop, directly lowering cost. \n\n4. Principled redundancy metric (SIV) with empirical support. The SIV-based selection aligns with semantic saliency: high-SIV regions correspond to foregrounds and yield higher accuracy than random or high-SIV pruning baselines. \n\n5. Comprehensive experiments and tangible efficiency gains. On ImageNet, SMixer and SMixer + DSTSP achieve comparable accuracy to Spiking Transformers while reducing memory and energy and boosting throughput; similar trends hold on CIFAR/DVS, time-series, and COCO detection. \n\n6. Analytical backing. The paper provides operation and energy models showing why spatial-temporal pruning reduces cost relative to the original mixer; it also contrasts DSTSP with weight-pruning methods on cost/accuracy/sparsity.", "weaknesses": "1.The robustness of the SIV (Social Influence Value) model under adversarial inputs or input perturbations has not been thoroughly investigated. This limitation raises concerns regarding the reliability of SIV in real-world applications, where data quality and integrity cannot always be guaranteed.\n2.While pruning effectively reduces model complexity and inference costs, it inherently leads to a reduction in model capacity. The extent to which this capacity loss can be mitigated by simply increasing the number of training epochs remains underexplored. Without a clear understanding of this trade-off, it is uncertain whether prolonged training truly restores the representational power of the pruned model or merely overfits to the remaining parameters.\n3.The paper does not clarify why DSTSP is intrinsically incompatible with the SSA architecture.\n4.DSTSP’s efficacy is only validated on small neuromorphic datasets; its scalability to large-scale, real-world benchmarks like HardVS remains untested.", "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": "pXBpLc3djk", "reviewer": "Reviewer_B5FE", "summary": "This paper proposes SMixer, a Spiking Neural Network architecture based on the Spiking-token Mixer paradigm, along with a Dynamic Spatial-Temporal Spiking Pruning (DSTSP) framework to reduce training overhead. The authors argue that rational SNN architectures should exhibit three key characteristics: fully event-driven operations, low training overhead, and competitive performance. They demonstrate that SMixer can replace Spiking Self-Attention mechanisms in various architectures while maintaining comparable performance, and that DSTSP can significantly reduce computational costs during training.", "questions": "See weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Well-motivated problem: The paper clearly identifies three important challenges in SNN design: the incompatibility of Spiking Transformers with asynchronous hardware, high training costs, and the need for competitive performance.\nComprehensive evaluation: The experiments span multiple domains (image classification, time series, object detection) and datasets (ImageNet, CIFAR, DVS-Gesture, COCO), demonstrating broad applicability.\nPractical approach: The DSTSP framework introduces no trainable parameters and uses simple addition-based operations, making it lightweight and aligned with SNN characteristics.\nSolid empirical results: The method achieves substantial reductions in GPU memory (to 76.44%) and energy consumption (to 53.03%) on ImageNet while maintaining or even slightly improving accuracy.\nGood ablation studies: The paper includes thorough ablations on pruning strategies, spatial-temporal ratios, and module placement.", "weaknesses": "Limited novelty of SMixer architecture: The Spiking-token Mixer concept appears to be directly adapted from STMixer (Deng et al. 2024). The main architectural contribution seems incremental—demonstrating that this existing architecture can replace SSA in other frameworks. The novelty primarily lies in the pruning framework rather than the architecture itself.\n\nTraining vs. inference confusion: The DSTSP framework is designed to reduce training overhead on GPUs, but the motivation emphasizes event-driven deployment on neuromorphic chips. There's a disconnect:\nSNNs are typically trained on GPUs anyway (as authors acknowledge)\nThe pruning ratios used during training may not reflect deployment scenarios\nHow does training with high pruning ratios affect actual neuromorphic deployment efficiency?\n\nPerformance degradation concerns:\nOn some tasks, there are non-trivial accuracy drops (e.g., 1.3% on SpikformerV2-8-384 T=4, 2% on QKFormer variants)\nOn COCO object detection, the 1.5% mAP drop is dismissed as \"acceptable,\" but this is subjective\nThe paper doesn't discuss scenarios where DSTSP might fail or provide guidelines for when not to use it\n\nWriting and presentation:\n\nThe term \"Spiking-token Mixer\" vs \"STMixer\" vs \"SMixer\" is used inconsistently\nFigure 5 could be clearer—the temporal pruning illustration is hard to parse\nSome claims lack support: \"The intrinsically low firing rates of SNNs result in significant network redundancy\" (line 83)—citation needed", "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": "OPjTHTW7gt", "reviewer": "Reviewer_2HdN", "summary": "The whole work focus on designing a completely lightweight spiking neural network, and calling on a paradigm for developing SNN model. Overall, the spiking-token token-mixer(SMixer), and a pruning method (DSTSP) significantly decrease the parameters that model needs and computational cost while mataining competitive performance comparing with main stream SNN models. Moreover, the pruning method can be effectively applied to several models to decrease cost.", "questions": "I have some doubts about vertical axis meaning in Figure 4. I'd like to know more details about the intuition of Figure 4. \n\nFor other questions, please refer to \"Weaknesses\".", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "In the article, the author concerned about some issues in SNN nowadays. Indeed, in the pursuit of more competitive performance in SNN, some architecture in ANN are widely used. Nevertheless, the author tried to stop such methods, and further propose a new paradigm to constrain construction for SNN. This is pioneering and full of originality. Moreover, the new structure and pruning method significantly reduce number of parameters and computational cost, which SNN aims to achieve all along, not to mention the two maintain similar performance with main stream models. As for writing skills, authors basically clarify their thought process to develop the new structure of SNN and pruning method with little confusion.", "weaknesses": "Firstly, in the preceding part of the article, the author mentioned Asynchronous Chip for several times. But no experiment about deployment on them was conducted.\nSecondly, as you present in Figure 6, I think it lacks persuasiveness just at first glance. Some objects' even can be harder to distinguish from background.\nLastly, in Table 2 (performance on ImageNet), if you add a item like \" Smixer+DSTSP\", it will be more persuasive.", "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": "e7LiV01b0v", "reviewer": "Reviewer_tRYC", "summary": "This study proposes a Spiking-token Mixer, which completely eliminates self-attention computation in SNN-based Transformers while maintaining comparable accuracy. Additionally, a DSTSP module is introduced to effectively improve training efficiency. Overall, the proposed approach demonstrates good generalizability and shows potential for deployment on edge neuromorphic hardware.", "questions": "1. The statement around line 166—“a model that uses only high-SIV tokens demonstrates considerably better accuracy than one relying on low-SIV tokens (97.9% vs 79.9%)”—needs clarification: which two models are being compared? To my knowledge, no mainstream SNN model should perform as poorly as 79.9% on this dataset.\n\n2. Around line 183, it is stated that SIV requires only a simple summation. However, ignoring the channel dimension, the number of required summations should be H × W × T. If this understanding is incorrect, clarification is needed. Additionally, because zero entries cannot be omitted, the claim that this operation is highly compatible with SNN computation seems unjustified—particularly since the paper does not discuss hardware implementation. This raises concerns regarding the true value of the Spiking-token Mixer.\n\n3. In Table 3, the reported accuracies of 98.12% and 98.43% on DVS128 are impossible, as the dataset contains only 288 test samples. Typically, such anomalies might arise if test-time ``droplast'' were enabled, but this is not the case here. Therefore, the authors must clearly explain how these erroneous accuracy values were obtained.\n\n4. Figure 6 indicates that a substantial amount of background noise persists even after applying DSTSP. Does this imply that the pruning strategy still has significant headroom for improvement? Theoretically, increasing pruning rates should not affect target features. How do the authors interpret this apparent inconsistency?\n\n5. Does the proposed DSTSP perform any operation during inference? If not, how is the change in activation size handled? If yes, wouldn’t such operations break the fundamental asynchronous timestep-by-timestep inference characteristic of SNNs? This requires clarification.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The proposed method fully avoids the computational cost of self-attention while achieving accuracy comparable to state-of-the-art models.\n\n2. The plug-and-play DSTSP module can be seamlessly integrated into various architectures, demonstrating strong applicability.", "weaknesses": "1. The analysis in Figure 4 lacks detail. In particular, rough explanations of RIE and MS-SSIM should be provided. Additionally, the claim that SIV in SNNs performs better than in ANNs may warrant theoretical justification, which should not be difficult to provide.\n\n2. The order of introducing DSSP and DSTP could be revised. The paper first mentions DSTP followed by DSSP, whereas the detailed descriptions appear in the opposite order. It is recommended to maintain consistency between textual structure and figure presentation.\n\n3. Table 5 appears to contain incorrect annotations. Does “E-SpikeFormer” refer to Spike-driven Transformer V3 (TPAMI)? It seems the table might have been copied, and the label “(This Work)” was not removed. In this case, once I-LIF neurons remove the temporal dimension during training, could the observed performance degradation be directly related to the ineffective functioning of DSTP? Further analysis is suggested.\n\n4. In this work, there is a trade-off between performance and pruning-based efficiency gains. However, the degradation in performance appears relatively large, while the improvement in efficiency is limited.\n\n5. Although the paper emphasizes deployment potential on brain-inspired edge hardware and points out the difficulty of QK matrix multiplication on such devices, no edge-hardware experiments are conducted.\n\n6. The DSTSP design is relatively simple—essentially the most basic pruning strategy in SNNs. If the pruning selection scheme were made more sophisticated, performance degradation could likely be reduced while providing greater acceleration.", "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)