TS-SNN: Temporal Shift Module for Spiking Neural Networks
Authors: Kairong Yu
Tianqing Zhang
Qi Xu
Gang Pan
Hongwei Wang
Venue: ICML 2025 poster (2025)
Keywords: Spiking Neural Networks
Neuromorphic Computing
Brain-inspired Learning
Primary area: applications->neuroscience_cognitive_science

Abstract: Spiking Neural Networks (SNNs) are increasingly recognized for their biological plausibility and energy efficiency, positioning them as strong alternatives to Artificial Neural Networks (ANNs) in neuromorphic computing applications. SNNs inherently process temporal information by leveraging the precise timing of spikes, but balancing temporal feature utilization with low energy consumption remains a challenge. In this work, we introduce Temporal Shift module for Spiking Neural Networks (TS-SNN), which incorporates a novel Temporal Shift (TS) module to integrate past, present, and future spike features within a single timestep via a simple yet effective shift operation. A residual combination method prevents information loss by integrating shifted and original features. The TS module is lightweight, requiring only one additional learnable parameter, and can be seamlessly integrated into existing architectures with minimal additional computational cost. TS-SNN achieves state-of-the-art performance on benchmarks like CIFAR-10 (96.72\%), CIFAR-100 (80.28\%), and ImageNet (70.61\%) with fewer timesteps, while maintaining low energy consumption. This work marks a significant step forward in developing efficient and accurate SNN architectures.

TS-SNN: Temporal Shift Module for Spiking Neural Networks
Kairong Yu" Tianqing Zhang"
Qi Xu²
Gang Pan' Hongwei Wang'
Abstract
them ideal for applications in neuromorphic computing and edge devices where energy efficiency is crucial Yamazaki et al., 2022; Taherkhani et al., 2020). In comparison to ANNs, which process information in static and continuous manner, SNNs inherently incorporate temporal dynamics by encoding information in the timing of spikes. Although SNNs are biologically inspired neural networks, there is still a sigmificant gap between current SNN models and the actual biological neural networks they aim to emulate. Neuromodulators like dopamine and norepinephrine are re- leased in response to previous neural activity and modify neuronal excitability and synaptic strength, influencing cur- rent and future activity (Turrigiano & Nelson, 2004; Alcamí & Pereda, 2019). Additionally, neurons adapt through mech- anisms such as rrequency-dependee synaptic plasticity and ion channel regulation, which allow them to respond based on their activity history and present status (Turrigiano & Nelson, 2004; Alcamí & Pereda, 2019). These mechanisms ensure that synaptic transmission is influenced by both past and future activity, enabling dynamic adjustments in the nervous system. Temporal modeling can be employed to handle the temporal information mentioned above. It is well-established technique in sequential data processing. However, due to the inherent temporal characteristics of SNNs, there is an overlap with video signal processing. This suggests that temporal modeling techniques used in other domains, such as video recognition, can be effectively applicd to SNNs. Although temporal modeling in SNNs is promising, effectively leveraging temporal information while maintaining low energy consumption remains signif- icant challenge. Previous methods have struggled to balance these two aspects. A major issue is that many existing methods either fail to fully exploit the temporal dimension or introduce substantial computational costs, negating the energy efficiency advantages of SNNs.
Spiking Neural Networks (SNNs) are increasingly recognized for their biological plausibility and energy efficiency, positioning them as strong al- ternatives to Artificial Neural Networks (ANNs) in neuromorphic computing applications. SNNs inherently process temporal information by lever- aging the precise timing of spikes, but balanc- ing temporal feature utilization with low energy consumption remains a challenge. In this work, we introduce Temporal Shift module for Spiking Neural Networks (TS-SNN), which incorporates a novel Temporal Shift (TS) module to integrate past, present, and future spike features within a single timestep via simple yet effective shift op- eration. A residual combination method prevents information loss by integrating shifted and origi- nal features. The TS module is lightweight, requir- ing only one additional learnable parameter, and can be seamlessly integrated into existing archi- tectures with minimal additional computational cost, TS-SNN achieves -fate-of-the-arl perfor- mance on benchmarks like CIFAR-10 (96.72%) CIFAR-100 (80.28%), and ImageNet (70.61%) %) with fewer timesteps, while maintaining low en- ergy consumption. This work marks a significant step forward in developing efficient and accurate SNN architectures.
1. Introduction
Spiking Neural Networks (SNNs) have emerged as promis- ing alternative to traditional Artificial Neural Networks (ANNs) due to their biological plausibility and energy effi- ciency. SNNs emulate the behavior of biological neurons, which communicate through discrete spikes rather than con- tinuous values. This spiking mechanism allows SNNs to op- erate with significantly lower power consumption, making
To address this challenge, we propose a novel approach that integrates temporal shift operations into SNNs, form- ing Temporal Shift module for Spiking Neural Networks (TS-SNN). The principle of the TS module is to distort spike features along the temporal dimension through a shift operation. This enhances the utilization of temporal infor- mation in SNNs, reducing the forgetting of past timestep information and improving the learning of future timestep information, all while introducing only minimal computa-
Equal contribution 'Zhejiang University "Dalian University of Technology. Correspondence to: Hongwei Wang <hongwei- wang @ www.iiiz.....oo. zjw.edu.cn>. Qi Xu <xuqi @ dlu..ccu..c2...
Proceedings of the 4rnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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TS-SNN: Temporal Shift Module for Spiking Neural Networks
tional cost. By integrating spiking features across different timesteps, TS-SNN effectively mitigates the impact of initial timestep information loss and addresses the issue of insuffi- cient feature extraction at end timesteps. This strengthens long-term temporal dependencies, thereby improving the accuracy of SNNs in related tasks. Our main contributions are summarized as follows:
et al., 2019; Lee et al., 2020; Wu et al., 2019; Fang et al., 2021a). Recent advancements in SNN architectures have in- troduced several improvement techniques. MPBN approach (Guo et al., 2023c) incorporates an additional Batch Normal- ization layer after membrane potential updates to stabilize training processes, DA-LIF (Zhang et al., 2025a) introduce an additional learnable decay on conventional LIF. (Duan et al., 2022) proposed the TEBN method, leveraging dis- tinct weights at each timestep to enhance learning dynamics, RSNN (Xu et al., 2024) enhanced spike-based data process- ing in SNNs. SEW-ResNa (Fang et al., 2021a) introduced a widely utilized ResNet backbone for SNNs, MS-ResNet (Hu et al., 2024) reorganizes the construction of Vanilla ResNet layers. (Zheng et al., 2023) explored spike-based motion estimation for object tracking through bio-inspired unsuper- vised learning. (Doutsi et al., 2021) introduced a dynamic image quantization mechanism enhancing visual perception quality over time by leveraging both time-SIM and rate- SIM for encoding spike trains. (Xu et al., 2023a;b; Yu et al., 2025a) proposed knowledge distillation methods to train deep SNNs using ANNs as the teacher model and SNNs as the student model. These innovations collectively contribute to the advancement of SNNs, making them more practical and effective for wider range of applications. While exist- ing research addresses various efficient method to enhance SNNs, the survey indicates the absence of an approach fuse features across different timesteps in SNNs.
We propose a Temporal Shift Module to address the inherent limitations in the temporal dynamics of tradi- tional SNNs. This module efficiently integrates past, present, and future spike features through simple shift operation, enhancing SNNs' temporal modeling capabilities with minimal additional computation. The TS module is easily incorporated into any SNN archi- tecture.
We introduces a residual combination method for the TS module, which integrates shifted and original fea- tures to prevent information loss after shifting. A penalty factor is utilized to maintain training stabil- ity, thereby enhancing the network's 's ability to capture temporal dynamies while preserving original features.
A series of extensive ablation studies were conducted to optimize the TS module and gain deeper insights into its effectiveness. The performance of TS-SNN was then evaluated on various benchmark datasets, demon- strating state-of-the-arr results with fewer timesteps. Additionally, computational energy consumption anal- yses were performed verify the energy efficiency of TS-SNN.
2.2. Temporal Shift Modeling
In deep learning, temporal modeling refers to the process of analyzing based on sequential data that evolves over time where the order and timing of data points are important. Due to the intrinsic temporal characteristics of SNNs, there is natural overlap with video signal processing, suggesting that temporal modeling techniques used in other domains, such video recognition, can be effectively applied to SNNs. Among traditional temporal modeling approaches, 3D CNNs are the most straightforward method et al., 2020; 2023a). While they can directly process spatiotempo- ral features, but they come with a high computational cost. To address this, (Lin et al., 2019) proposed TSM, which transforms 3D CNNs into 2D CNNS by shifting small portion of channels along the temporal dimension, thereby achieving the performance of 3D CNNs at the cost of 2D CNNs. Inspired by the principles behind TSM. ACTION- Net (Wang et al., 2021) introduced a three-path architecture for enhanced action recognition, while the Learnable Gated Temporal Shift Module (LGTSM) (Chang et al., 2019) in- corporated learnable kernels to improve temporal fusion efficiency. (Wu et al., 201 8a) combined the shift operation with 1 x 1 convolutions as an efficient alternative to 3 x 3 convolutions. Further ad advancements include the proposal of learnable active shifts by researchers (Chen et al., 2019; Jeon
2. Related Works
2.1. Training of Deep SNNs
SNN training typically follows two approaches: ANN-to- SNN conversion and direct SNN training. The former con- version methods enable the utilization of existing ANN models has been made a lot works (Han & Roy, 2020; Wang et al., 2023a) but have limitations in fully leveraging the spatio-temporal information intrinsic SNNs due to the inherent absence of this dimension in ANNs (Hu et al., 2024). Direct SNN training methods, particularly unsu- pervised learning approaches like Hebbian learning(Hebb, 2005) and Tiaiee-Tiinn---eee Poo, 1998), are limited in scalability for deep networks and large datasets. To address this, (Chakraborty et al., 2021) proposed hybrid model combining unsupervised STDP with backpropagation for energy-efficient object detection.
Plasticity (BTDP)BBi &
Directly learning SNNs is challenging due to the undif- ferentiable nature of spike firing. The surrogate gradient (SG) approach addresses this issue, thereby enhancing the practicality and applicability of direct SNN training (Neftei
2

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TS-SNN: Temporal Shift Module for Spiking Neural Networks
pad zero
temporal shift
Channel C
past
future
truncate
present
Timestep
(a)
(b)
(c)
(d)
Figure 1. Illustration of Temporal Shift. (a) Initial spike feature representation, where each row represents different channel and each column represents a different timestep. (b) Temporal shift operation. Some channel are shifted forward (to the right) along the temporal dimension, while others are shifted backward the left), with the remaining features left unchanged. (c) To maintain consistent tensor dimensions after the shift, the empty spaces created by the shift are padded with zeros and excess values are truncated. (d) The shifted feature provides fusion of past, present, and future information within each channel.
Kim, 2018), and (Liu et al., 2021; Zhang et al., 2025b) implemented a self-attention mechanism through shifting windows enhance the performance vision transformers. (Yu et al., 2022) combined spatial shifts with multilayer per- ceptrons achieve competitive performance in high-level vision tasks. (Yu et al., 2025b) proposes spatiotempo- ral module leveraging frequency characteristics to enhance network performance. Additionally, (Li et al., 2023) ex- tended the shift operation to grouped spatial-temporal shifts, demonstrating its applicability video restoration tasks. These advancements in related studies demonstrate the cessity and effectiveness of temporal modeling. However, current research on SNNs this domain is still quite lim- ited. Our proposed method aims to fill this gap.
The Heaviside step function + is defined as:
0 i < 0
((x)
(2)
if 2
Where 1/--1,n represents the membrane potential after spike trigger at the previous timestep, X is the input feature of the 1-th layer at timestep t. while V denote the integrated membrane potential. Uin is the firing threshold to determine whether the spike St." triggered.
During the network training of SNN, we utilize a spatial- temporal backpropagation (STBP) (Wu al., 2018b) al- gorithm. The independently descriptions of temporal and spatial aspects are follows:
3. Methodology
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aL ast
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In this section, we first provide brief overview of the Leaky antegrate-and-Fir (LIF) model and the training process discussed. Following that, we introduce the proposed TS module. Lastly, we described how to implement the TS module in SNN.
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Os
(3)
0L OL ast aL as
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Although the Heaviside Function 2 is non-differentiable, previous work has successfully overcome this through the surrogate gradients (SG) method (Neftci et al., 2019):
3.1. Preliminary of the Leaky Integrate-and-Fing Model and SNN Deep Training
The spiking neuron is fundamental component of SNNs and the Leaky Integrate-and- due to its efficiency and simplicity. Mathematically, the discrete-time and iterative representation of LIF neuron are described as follows:
as 0V = siiin - VAN 2
a
(LIF) neuron employed
(4)
where both and Vn are hyperparameters usually set to 1. The gradient takes the value of when V is within the range of -0.5 to 0.5, otherwise takes the value of O.
3.2. Temporal Shift Module
Vin= = 1,. xt:m),
(1a) The TS module enhances temporal dynamics in SNNs by (Ib) shifting portions of the feature map along the temporal di- mension. It divides the input feature tensor into segments (Ic) and shifts them: some forward, some backward, and others
St. e(vim- -it);
H Vieser S5 V. (1 St.a).
3

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TS-SNN: Temporal Shift Module for Spiking Neural Networks
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(a) LIF models TS-SNN
(b) vanilla SNN architecture
(c) SNN architecture with module
Figure 2. Overview of the TS-SNN Architecture
unshifted, representing shifts of +1, -1, and 0 timesteps, respectively.
Algorithm Temporal Shift Module
Input: Channel Folding Factor C2; Input tensor X of shape [T, C, H, W1, where:
The TS module is designed to efficiently model temporal dynamics within SNN by shifting portions of the feature map along the temporal dimension. The Temporal Shift operation splits the input feature tensor into multiple seg- ments and then assigns two random split points to divide the original features into three parts. These segments are then shifted along the temporal dimension: some forward, some backward, and others unshifted, representing shifts of +1, -1, and timesteps. respectively.
T: Timesteps, C: Channels, H: Height, W: Width.
Output: Shifted tensor Z.
Method:
Compute fold channel size Cjoid C/C².
Generate random indices T for channel groups.
Determine 9i and 92 from 1y:
91 + min [0]. [,(1]), 92 + m (0), [y(1 Initialize a zero tensor Z with the same shape as X. Shift a portion of left:
Formally, for an input tensor X E XxxCX** where T is the number of timesteps, C is the number of channels, H is the height, and W is the width. First, C' channels are divided into Cs feature segments, each containing Cjold channels, defined as:
Z|: 1, 9) x Cjaaa : + X[1 ;, g1 x Cyotd in: :].
Shift a portion of X right:
7 9 x Cjold 92 CfoM : X1: 1, Crou
92 x :...
Leave the remaining portion of X unchanged:
Z 92 x Gjol i. +- X [[.99 X Cjold i, Reshape Z back to [T,C, C, W]
Cjold = C./Ck.
(5)
Next, the tensor Z after the temporal shift is defined as:
return shifted tensor Z.
Z ++1 X[t + 1,: 91 Cyatd:;;.] :,:] -ー X[t 1,91 Cjold : 92 Cjold i]
(6)
are truncated shown in Figure 1(c). Figure 1(d) illus- trates the multi-timestep fused feature matrix after the shift, indicating how the "past" (previous timesteps), "present" (current timestep), and "future" (upcoming timesteps) are fused within a single timestep. This fusion forms a complete data representation by stacking partial features from differ- ent timesteps. This TS operation can be performed without any multiplications or additions, thereby introducing no ad- ditional computational burden. Compared traditional 2D convolution operations, the TS module achieves the most straightforward form of feature fusion, which aligns well with the energy energy-efficient and low-power nature of SNNs. The detailed Temporal Shift procedure for the TS-SNN is given in Algorithm
7 X(t, 92 Ojoia :,
Where 91 and 92 represent the indices of the feature seg- ments after random split, with 0 < 91 < 92 < Cs and I1 # 92- In Figure 1(a), the initial input spike feature maps with C channels and timesteps are depicted. In Figure 1(b), some channels are shifted forward by an oper- ation of +1, others backward by an operation of -1, while the rest remain unshifted along the temporal dimension demonstrating the process of Equation (6). However, this shift operation can result in vacancies or excesses at the edge timesteps where features are shifted away or out. To maintain consistent tensor dimensions after the shift oper- ation, vacancies are padded with zeros, and excess values
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TS-SNN: Temporal Shift Module for Spiking Neural Networks
3.3. Implementation of the TS Module
72.0
71.8
To integrate the TS module effectively into the network architecture, common approach is to directly insert the module in place. However, this straightforward method can hinder the model's ability to learn the original features, es-
71.6
71.4
Accuracy
71.2
71.0
pecially when a significant portion of channels is shifted, resulting in the loss of valuable original information at the current timestep. To address this issue, as shown in Fig- ure 2(c), the shifted spike features Z are combined with the original input X through residual shift insert the TS module, mitigating the information loss caused by the shifting operation. This residual shift method preserves the original features while allowing the network to capture temporal dynamics. Mathematically, the residual shift is defined as follows:
70.8
8
16
32
Channel Folding Factor(C.)
Figure 3. Impact of Channel Folding Factor (Ct) on CIFAR-100 Accuracy with ResNet20 architecture. The accuracy peaks when Cj is set to 32.
are standard benchmarks for image classification, consist- ing 50,000 training images and 10,000 testing images, sized 32 x 32. CIFAR- 10 comprises 10 classes, while CIFAR- 100 contains 100 classes. ImageNet (Deng et al., 2009) is large-scale dataset extensively used for bench- marking image classification algorithms. It includes 1.2 mil- lion training images, 50,000 validation images, and 100,000 test images, categorized into 1,000 distinct classes represent- ing a wide range of objects. CIFARIO-DVS (Li et al., 2017b) is a neuromorphic dataset derived from the frame-based CIFAR-I0 dataset using a dynamic vision sensor (DVS), It comprises 10,000 event streams, each sized 128 x 128, divided into 10 categories with 1,000 samples per class. We follow the convention of previous studies (Wang et al., 2023b) by splitting the dataset into training and testing sets in a 9:1 ratio.
= X.
(7)
where a is the penalty factor that constrains the temporally shifted tensor to prevent training instability or excessive spiking activity, typically set between 0.2 and 0.5. And denotes element-wise multiplication. This residual shift combines original and shifted spike features with a weighted factor to prevent information loss, enhancing stability dur- ing training. It also alleviates the problem of learning degra- dation in the transmission of spiking temporal features. By integrating historical and future features through the shifting operation, this approach emphasizes specific features of the current timestep. This method ensures that while the tempo- ral shifts incorporate features from multiple timesteps, the complete information of the original timestep is preserved, thereby minimizing the loss of information during spike transmission.
Implementation Details. The entire codebase was imple- mented in PyTorch this study. Experiments on CIFAR-10 10, CIFAR-100 and CIFARIO-DVSS were conducted using an NVIDIA RTX 3090 GPU, while experiments ImageNet were performed using 8 NVIDIA RTX 4090 GPUs. Key hyperparameters, such the firing threshold uin, were set to 1.0. The channel folding factor C₃ was set to 32, and the shift operations followed the sequence: left, right, no shift. The default value of the penalty factor a was 0.5. The optimization process utilized the SGD optimizer with momentum of 0.9, an initial learning rate of 0.1, and CosineAnneal learning rate adjustment strategy. The total number of training epochs was set to 500 for 10FAR-10, CIFAR-100, and CIFARIO-DVS, and 300 for ImageNet.
4. Experiments
To validate the effectiveness of the proposed TS-SNN, we evaluate its performance across four datasets, and various network architectures. First, we provide details on the datasets and implementation specifics, Next, we present extensive ablation experiments to optimize the TS module. Subsequently, we compare the performance of TS-SNN with state-of-the-ard methods on static image classification tasks and event-based vision tasks. Afterward, we evaluate the generality of the TS module Transformer-bar based ar- chitecture. Finally, we analyze the computational efficiency of the proposed method. More details on the datasets, hy- perparameters, additional experiments are provided in the Appendix.
4.2. Ablation Studies
To optimize the TS module and gain deeper insights into its effectiveness, we conducted a series of ablation studies. These experiments were designed to isolate and evaluate the impact of various components within the TS-SNN architec- ture, leading to the optimal configuration.
4.1. Experimental Setup
Datasets.
The proposed method was evaluated on four datasets: CIFAR- 10, CIFAR-100 ImageNet, and CIFAR 10-
DVS. CIFAR-10 10 and CIFAR-100 (Krizhevsky et al., 2010)
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TS-SNN: Temporal Shift Module for Spiking Neural Networks
72.6
72.52
72.5
72.49
72.43
72.4
72.29
Fixed-Length Transformations
Scalable
72.24
72.22
Figure Comparison of Fixed-Length (left) and Scalable Random Transformations (right) in Temporal Feature Shift.
72.1
72.0
(L-R-0)
(1-D-R) (R-L-0)
[R-0-L]
Shift Direction Combination
Impact of Channel Folding Factor on Model Accuracy The principle of the TS module distort spike features along the temporal dimension. Different levels of distortion correspond varying degrees of temporal spike feature integration. To facilitate block-wise feature movement, we introduce the channel folding factor, Ck, which controls the number of feature groups within the channels. The size of each group determined by C/Cg. larger C₂ results in smaller individual feature blocks, since 9I and 92 are randomly generated from the group, smaller groups contain less shifting information. Conversely, smaller Cş lead to richer shifting information within each group. Therefore, selecting an appropriate C₃ is crucial for optimal module performance. We evaluated this impact by testing the ResNet20 architecture on the CIFAR-106 dataset with a timestep 2. The results, presented in Figure 3, show that varying C affects performance by up 0.96%. Ultimately, the default channel folding factor set to in method.
Figure 5. Distribution accuracy with different TS direction com- binations, with 0, I,, and R representing shift, left shift and right shift, respectively.
94.01
94.68
in training only training inference
90.0
Accuracy (%)
85.0
81 81,20
80.0
72,60 73,12
70.0
CFAR-10
CIFAR-100 Datasets
GIFRLO DVS
Figure 6. Accuracy improvement with consistent Module ap- plication across training and inference stages.
figuration of left-right-no shift, yields the highest accuracy at 72.52%. Consequently, this combination (L-R-0) has been default shift direction combination all experiments conducted in this study.
Temporal Channel Feature Shift Strategy This study compares two strategies for the temporal feature shift method: scalable random transformation and fixed-length transformation. The primary difference between these meth- ods lies in their approach splitting and moving feature seg- ments across timesteps, depicted in Figure 4. The fixed- length transformation consistently shifts uniform number of features, while the random transformation method varies the intervals randomly during each shift, leading to differ- ent fusion ratios between features from adjacent timesteps. To evaluate their effectiveness, we conducted experiments on the CIFAR-100 dataset under identical conditions. The results show that the fixed-length transformation achieved an accuracy of 71. 11.24% while the random transformation outperformed it with an accuracy of 71.63%. These findings suggest that the random transformation method provides better accuracy than the fixed-length transformation.
Impact of Consistent TS Module Application We eval- uated the effect of applying the TS module solely during training compared applying it consistently across both training and inference stages. As shown in Figure 6, consis- tent application results in higher accuracy across all three datasets. For instance, CIFAR 10-DVS, accuracy im- proves from 81.20% to 83.60%. Notably, this performance gain does not increase computational costs, as the FLOPs remain unchanged in both scenarios. These results highlight the effectiveness of using the TS module in both training and inference to enhance temporal modeling while maintaining efficiency.
4.3. Comparisons with Other Methods
Impact of TS Direction Combinations on Model Aceu- racy The data presented in the Figure 5 above shows the accuracy results based on various combinations of temporal shift directions applied to different channel features. Com- binations correspond to different arrangements of left, right, and present temporal shifts. The results indicate that the combination labeled as (L-R--0), which involves the con-
Static Image Classification. We evaluated our model on three static datasets: CIFAR-10. CIFAR-100, and ImageNet. The TS Module was integrated into ResNet-19 19 and ResNet- 20 for CIFAR-10 and CIFAR-100, and into ResNet-18 and ResNet-34 for ImageNet. The results on CIFAR-10/100 are summarized in Table 1, with top-1 accuracy reported as the mean and standard deviation of 3 trials. On CIFAR-10.
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TS-SNN: Temporal Shift Module for Spiking Neural Networks
Table I. Comparison results with SOTA methods CIFAR-10/100
CIFAR-10
CIFAR-100
Methods
Architecture
Timestep
Accuracy
Architecture
Timestop
Accuracy
SNNDS-))NN(0026)
ResNet-19
4
95.53%
ResNet-19
74.10%
ResNet-19
2
94.16%
ResNet-19
72.87%
TET(2021)
ResNet-19
94.44%
ResNet-19
74.47%
ResNet-19
6
94.50%
ResNet-19
74.72%
ResNet-19
94.41% %
ResNet-19
76.32%
LSG(2023)
ResNet-19
4
95.17%
ResNet-19
76.85%
ResNet-19
6
95.52%
ResNet-19
77.13%
ResNet-19
2
95.60%
ResNet-19
76.70%
PFA(2023)
ResNet-19
4
95.71%
ResNet-19
78.10%
ResNet-19
6
95.70%
ResNet-19
79.10%
ResNet-20
5
91.78%
ResNet-20
64.07%
Diet-SNN(2023)
ResNet-20
92.54% 10
ResNet-19
2
93.85%
IM-loss(2022g)
ResNet-19
4
95.40%
ResNet-19
95.49%
ResNet-19
1
96.06%
ResNet-19
78.71 %
ResNet-19
2
96.47%
ResNet-19
79.51
ResNet-19
4
96.52%
ResNet-19
80.10%
MPBN(2023c)
ResNet-20
1
92.22%
ResNet-20
68.41%
ResNet-20
2
93.54%
ResNet-20
70.79%
ResNet-20
94.28%
ResNet-20
72.30%
ResNet-19
3
95.29%
ResNet-19
77.21%
IM-LIF(2024)
ResNet-19
6
95.66%
ResNet-19
77.42%
ResNet-19
1
96.50%
=0.08%
ResNet-19
78.61 %
ResNet-19
2
96.72% +000%
ResNet-19
80.25 =0.0%%
Ours
ResNet-20
1
93.03 % 12.12%
ResNet-20
69.02 % =0.118
ResNet-20
2
94.11 % 1.0.0%%
ResNet-20
71.83 % 10.10%
ResNet-20
4
94.71% %
ResNet-20
73.46 %
both ResNet-19 and ResNet-20 achieve the highest accu- racy across all time steps. For CIFAR-IOM 100, our method surpasses others in all configurations except for ResNet-19 at timestep where it lags behind MPBN. The possible reason is that at timestep 1, only the no-shift condition is present, which doesn't t fully leverage dynamic temporal in- formation, limiting its impact on performance compared to later timesteps. On the more challenging ImageNet dataset as summarized in Table 2, ResNet-34 achieves an optimal accuracy of 70.61% % with timestep of 4, marking sig- nificant improvement. These results show that method performs excellently with fewer time steps, demonstrating its effectiveness. The results in Table demonstrate that when using a single timestep (T=1), TS theoretically should not exert any functional effect. However, we observe con- sistent performance improvements under this configuration. This phenomenon can be attributed the scaling factor a in Equation (7). Although temporal shift operations are nullified in the spiking feature dimension with T=1, the adaptive scaling mechanism parameterized by still in- troduces non-trivial modifications to the spiking features,
which may contribute to the observed performance gains.
Event-based Vision classification. To comprehensively evaluate the spatiotemporal processing capabilities of TS- SNN, we tested the model on the CIFAR10-DYV dataset, which, unlike static datasets, includes a temporal dimen- sion. The experimental results are summarized Table 3. Our model outperformed SOTA methods, achieving an ac- curacy of 83.90% with 16 timesteps, and 83.80% with only 10 timesteps, which demonstrate that TS-SNN can surpass SOTA performance while using significantly fewer timesteps. More importantly, these results highlight the su- perior performance of the TS module in handling longer timesteps. indicating its effectiveness in mitigating informa- tion loss over long sequences.
4.4. TS Module on Transformer-Bant Basse Architectures
To evaluate the generality of the TS module, we integrated it into Spikeformer-4-366 a transformer-ban architec- ture(Zhou al., 2022). Experiments on CIFAR-10 and

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TS-SNN: Temporal Shift Module for Spiking Neural Networks
Table Comparison results with training hased SNN SOTA
Table 3. Comparison results with SOTA methods on CIFARIO- DVS. T denotes timestep.
ImageNet. T denotes Timestep.
Methods
Architecture
Accuracy
Methods
Architecture
Accuracy
STBP-tdBN (2021)
ResNet34
63.72%
IM-loss(2022a)
ResNet-19
10
72.60%
TET (2021)
ResNet34
64.79%
LSG(2023)
ResNet-19
10
77.90%
RecDis-SNN (2022b)
ResNet34
67.33%
MPBN(2023c)
ResNet-19
74.40% 10
GLIF (2022)
ResNet34
67.52%
MPBN(2023c)
ResNet-20
78.70% 10
IM-Loss (2022a)
ResNet18
67.43%
TET(2021)
VGGSNN
77.30%
ResNet18
63.68%
IM-LIF(2024)
VGG-13
10
80.50%
Real Spike (2022c)
ResNet34
67.69%
GIIIF(2022)
7B-wideNet
16
78. 10%
ResNet18
63.03%
RMP-Loss (2023b)
STSA(2023b)
STS TTS-fraasformer
16
79.93%
ResNet34
65.17%
Spikeformer(2022)
Spikeformer
16
80.90%
ResNet18
63.14%
MPBN (2023c)
ResNet34
64.71% 1%
SEW(2021a)
SEW-ResNet
74,40%
ResNet18
63.18%
PLIF(2021b)
PLIFNet
74.80%
SEW ResNet (2021a)
ResNet34
67. 67.04%
10
83,80 % 20.000
Ours
ResNet-20
ResNet18
68.18
83.90 2:0.0000
Ours
ResNet34
70.61 % 1:0200
Table 4. Results on CIFAR-10 10 and CIFAR-100 with and without TS module Transformer-hasee architecture.
Accuracy
Methods
CIFAR-10 CIFAR-100
CIFAR-100 datasets demonstrate its effectiveness, as shown in Table The TS module enhanced accuracy on both datasets. with a notable +0.38% gain on CIFAR- 100. These results highlight its ability to integrate temporal features ef- fectively, even in transformer-basee models, demonstrating its adaptability across diverse neural network architectures.
Spikeformer-4-384
95.19%
77.86%
Spikeformer-4-384 with TS
95.28%
78.24%
Table S. Computational consumption for processing single sam- plc of CIFAR-100 ImageNet, and CIFARIO-DVS.
4.5. Analysis of Computation Efficiency
Dataset
CIFAR-100 CIFARIO-DVS
ImageNet
Timestep
10
4
In this section, the energy cost is estimated based the number of operations in 45-mm technology during single-image inference. The number of multiplication-and accumulation operations (MACs) remains constant for a given network in ANNs, while in SNNs, computations are primarily accumulation operations (ACs) triggered by in- coming spikes. The energy consumption for both ANN and SNN models is based on the number of MACs and ACs(Horowitz, 2014), where each of the operation consume 4.6 pJ and 0.9 pJ, respectively(Qiat et al., 2015). The com- putational energy consumption across three datasets is sum- marized Table The method this experiment is based on (Chen et al., 2023), where the energy consumption of SEW ResNet18 reported to be 13.10 mJ. Notably, Under the consistent experimental conditions, SNN consumes only 5.857 mJ and results for other datasets are also significantly low. This substantial reduction in energy consumption may be attributed to the sufficiently high spiking rate, demonstrating the clear energy efficiency advantage of our proposed method.
Architecture
ResNer20
ResNer20
SEW ResNet18
ACs
141.2M
1.23G
1.62G
MACs
53.99M
514.81M
956.4M
FLOPs
868.36M
8.65G
7.29G
Param
11.3M
11.2M
11.6M
Energy
0.375mJ
3.475mJ
5.857mJ
5. Conclusion
Building on deep understanding of SNNs and inspired by temporal modeling and neuroscience, this paper introduces novel Temporal Shift Module for SNNs. This mod- ule achieves simple yet effective fusion of past, present, and future spike features within each one timestep through a shift operation. By introducing minimal computational cost, it effectively reduces the forgetting of past timestep information, enables learning from future timesteps, and
proposed TS-
8

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TS-SNN: Temporal Shift Module for Spiking Neural Networks
establishes robust long-term temporal dependencies well. We conducted experiments four datasets and performed extensive ablation studies, demonstrating that the proposed TS module significantly improves SNN accuracy. As plug- and-play module, shows great potential for widespread application. Additionally, energy consumption results indi- cate that TS-SNNs are highly energy-efficient.
Chen, W., Xie, D., Zhang, Y., and Pu, S. All You Need Is Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification. pp. 7241-7250, 2019, URL http:///pppmococccccccccc//oooce
9/html/Chen_All You_Ned Feee_lsss______iiiiieeeeee Designing Efficient. Convolutional Cetvoluuional..CP.... 2019 100011pppppe paper..m....
Deng, H., Zhu, R., Qiu, X., Duan, Y., Zhang. M., and Deng, L. Tensor Decomposition Based Attention Module for Spiking Neural Networks, October 2023. URL http:
Acknowledgements
This work was supported in part by National Natural Sci- ence Foundation of China (NSFC) (62476035, 62206037, 62276230, 62271361, U24B20140), and Natural Science Foundation of Zhejiang Province (LDT23F02023002) and State Key Laboratory (SKL.) of Biobased Transportation Fuel Technology.
/aaxiv.org/abs/2310..444 arXiv:2310.14570 [cs].
Deng. J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei- Fei, L. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vi- sion and Pattern Recognition, pp. 248-255, June 2009. doi: 10. 200.. http:///ww..tee.ooooooooooodonnn ISSN: 1063-6919.
URL https:
Impact Statement
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
Deng, S., Li, Y., Zhang, S., and Gu, Temporal Efficient Training of Spiking Neural Network via Gradient Re- weighting. In Proceedings of ICLR, October 2021, URL
References
Doutsi, E., Fillatre, L., Antonini, M., and Tsakalides, P. Dynamic Image Quantization Using Leaky Integrate-and- Fire Neurons. IEEE Transactions Image Processing, 30:4305 2021. ISSN 1941-0042 doi: 10.1 10911 P.2021 220221.07701 3370193. URL ment/9399803. Conference Name: IEEE Transactions on Image Processing.
Alcamí, P. and Pereda, A. Beyond plasticity: the dynamic impact of electrical synapses on neural circuits. Nature Reviews Neuroscience, 20(5):253-271, May 2019, ISSN 1471 1471-0048, doi: 10. 10.11038/441583-019-0111
URL
Bi, G.-q. and Poo, M.-m. Synaptic modifications in cul- tured hippocampal neurons: dependence spike timing, synaptic strength, and postsynaptic cell type. Journal of neuroscience, 18(24): 10244--044441047, 1998. ISBN: 0270- 6474 Publisher: Soc Neuroscience.
Duan, C., Ding, J., Chen, S., Yu, 7.,, and Huang, T. Tem- poral Effective Batch Normalization in Spiking Neural Networks. Advances in Neural Information Process- ing Systems, 35:34377-34390, December 2022. URL http:://pocceediiggsseuerrpppppp onference.htm html.
Chakraborty, B., She, X., and Mukhopadhyay, S. A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection. IEEE Transactions on Image Pro- cessing, 30:9014-9029, 2021. ISSN 1941-0042. doi: 10.1 10.11099/1P2222133122200 URL https://ieccxplore.i cee.org/doccmeen/9991.00 Conference Name: IEEE Transactions on Image Processing.
Fang, W., Yu, Z., Chen, Y., Huang, T., Masquelier, T., and Tian, Y. Deep Residual Learning in Spiking Neural Net- works. Proceedings of NeurIPS, 34:21056-21069, 2021a.
URL fe4
Chang, Y.-L., Liu, Z. Y., Lee, K.-Y., and Hsu, W. Learnable Gated Temporal Shift Module for Deep Video Inpaint- ing, July 2019, URL http://arxiv.org/abs// http:///a//199.011//// arXiv:1907.014. 101 [cs].
Fang. W., Yu, Z., Chen, Y., Masquelier, T., Huang, T., and
Tian, Y. Incorporating Learnable Membrane Time Con- stant To Enhance Learning of Spiking Neural Networks. In Proceedings of ICCV, pp. 2661-2671, 2021b. URL https://openaccess www.aaawcccsss.teee....ooooo theccf.com///////o ttcccf..omm/oome////ccc/// //ttt/////
Chen, G., Peng, P., Li, G., and Tian, Y. Training Full Spike Neural Networks via Auxiliary Accumulation Pathway, January 2023. URL http://akiivorr/ab//233111119 arXiv:2301.11929 [es].
Fang Incorporating Learnable Membrane Semmre Const
Learning Sfanninggof.Spiiiiie Spikin.p ICCV JCC,2201.c
er.html.

---

TS-SNN: Temporal Shift Module for Spiking Neural Networks
Guan, B. and Zhao, J. Relative pose estimation for multi- camera systems from point correspondences with scale ratio. In Proceedings of the 30th ACM International Conference on Multimedia, pp. 5036-5044 2022.
He, K., Zhang, X., Ren, S., and Sun, J. Deep Residual Learning for Image Recognition. pp. 770-778, 2016. URL https://openaccess. wwt.s//ccc...cooooeetttiito Sinl/HeDDeepp Dew..eeeeeeuelulll..eeee tml.
Guan, B., Zhao, J., Li, Z., Sun, F., and Fraundorfer, F. Minimal solutions for relative pose with single affine correspondence. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pp- 1929-1938, 2020.
Hebb, D. O. The organization of behavior: A neuropsycho- logical theory. Psychology press, 2005. ISBN 1-135- 63191-3.
Horowitz, M. 1.1 Computing's energy problem (and what we can do about it). In 2014 IEEE International Solid- State Circuits Conference Digest of Technical Papers (ISSCC), pp. 10-14, February 2014. doi: 10. 10.1 1099/S SCC.2014.6757323, URL https://eecepploeeieee .orgo////aeee.rr//abbbb.. strac/dlccumen66757333. ISSN: 2376-8606.
Guo, J., Ouyang, W., and Xu, D. Multi-dimensional pruning: A unified framework for model compression. In CVPR, 2020.
Guo, J., Xu, D., and Ouyang, W. Multidimensional pruning and its extension: A unified framework for model com- pression. IEEE Transactions on Neural Networks and Learning Systems, 2023a.
Hu, Y., Deng, L., Wu, Y., Yao, M., and Li, G. Ad- vancing Spiking Neural Networks Toward Deep Resid- ual Learning. IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15, 2024. ISSN 2162- 2388. doi: 10.1 10.//99/N 2024.335333 URL https://iccexplore.ii ieee. wwwp//astaat/oocmmm///////// Conference Name: IEEE Transactions Neural Net- works and Learning Systems.
Guo, Y., Chen, Y., Zhang, L., Liu, X., Wang, Y., Huang, X., and Ma, Z. IM-Loss: Information Maximization Loss for Spiking Neural Networks. Proceedings of NeurIPS, 35: 156-166, December 2022a. URL https:/procceedings.og httss//paper22222/hhh11100660600 (cce@be02e7adB8dde Abstract- Cwnference.eneeeeeeeeeee html.
Guo, Y., Tong, X., Chen, Y.. Zhang, L., Liu, X., Ma, Z., and Huang, X. RecDis- SNN: Rectifying Membrane Po- tential Distribution for Directly Training Spiking Neural Networks. In Proceedings of CVPR, pp. 326-335, New Orleans, LA, USA, June 2022b. IEEE, ISBN 978-1- 66546-946-3. doi: 10. 0WW.CVVPRR22682222200000000 URL https://eeeeoooooe...eeccc//oeee
Jeon, Y. and Kim, J. Constructing Fast Network through Deconstruction of Convolution. In Advances in Neural Information Processing Systems, volume 31. Curran As- sociates, Inc., 2018. URL https:/ppooceedigs.ncrrpp /paper/201 8/hash///1 http:/99900000000070000000011110 3- Absttract.hml..
Guo, Y., Zhang. L., Chen, Y., Tong, X., Liu, X., Wang, Y., Huang, X., and Ma, Z. Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks. In Proceedings of ECCV, Lecture Notes in Computer Science, pp. 52- 68, Cham, 2022c. Springer Nature Switzerland. ISBN 978-3-031-19775-8 doi: 111110097788-003-199777
Krizhevsky, A., Nair, V., and Hinton, G. Cifar- (canadian institute for advanced research). URL http://www: cs. toronto. edu/kriz/cifar himl, 5(4):1, 2010.
Lee, C., Sarwar, S. S., Panda, P., Srinivasan, G., and Roy, K. Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures. Frontiers in Neuro- science, 14, 2020. ISSN 1662-453X. URL https://www.
Guo, Y., Liu, X., Chen, Y., Zhang, L., Peng, W., Zhang, Y., Huang, X., and Ma, Z. RMP-Loss: Regularizing Mem- brane Potential Distribution for Spiking Neural Networks, August 2023b. URL http://aarkv..rrraa//2333.8711 arXiv:2308.0678) [cs].
Li, D., Shi, X., Zhang, Y., Cheung, K. C., See, S., Wang, X., Qin, H., and Li, H. A Simple Baseline for Vidco Restoration with Grouped Spatial-Temporal Shift. In 2023 IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), pp. 9822-9832. Vancouver, BC, Canada, June 2023. IEEE. ISBN 9798350301298. doi: 10. 10.1100//CPRR22220 001.7729.222.000999 00947.. URL https:
Guo, Y., Zhang, Y., Chen, Y., Peng. W., Liu, X., Zhang, L.., Huang. X., and Ma, Z. Membrane Potential Batch Nor- malization for Spiking Neural Networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 19430, October 2023c.
siccexporr.iee.oggdccmmm///////
Han, B. and Roy, K. Deep Spiking Neural Network: Energy Efficiency Through Time Based Coding. In Proceedings of ECCV, Lecture Notes in Computer Science, pp. 388- 404, 2020. doi: 10.1007/978-
Li, H., Liu, H., Ji, X., Li, G., and Shi, L. Cifar10-dvs: an event-stream dataset for object classification. Frontiers neumsccincee 1 11:309, 2017a
23.
10

---

TS-SNN: Temporal Shift Module for Spiking Neural Networks
Li, H., Liu, H., X., Li, G., and Shi, L. CIFARIO-DVS: An Event-Stream Dataset for Object Classification. Frontiers in Neuroscience, 11, 2017b. ISSN 1662-453X. URL
00141/full. Publisher: Frontiers.
Rathi, N. and Roy. K. DIET- A Low-Latency Spik- ing Neural Network With Direct Input Encoding and Leakage and Threshold Optimization. IEEE Transac- lions on Neural Networks and Learning Systems, 34 (6):3174-1182, June 2023. ISSN 2162-2388. doi: URL https://ieee Confer-
00309.
Li, Y., Lei, Y., and Yang, X. Spikeformer: A Novel Ar- chitecture for Training High-Performance Low-Latency Spiking Neural Network, November 2022. URL http: //arxiv.org/ab/22211 10666. arX1v:2211.10666 [cs].
10. 10.1109/TNNLS,000 2021.31112221111189991
splore.ieee wwreeeerraasssrcccdccceeettStSee ence Name: IEEE Transactions on Neural Networks and Learning Systems.
Lian, S., Shen, J., Liu, Q., Wang, Z., Yan, R., and Tang, H. Learnable Surrogate Gradient for Direct Training Spiking Neural Networks. In Proceedings of IJCAL. pp. 3002-3010, Macau, SAR China, August 2023, ISBN 978- 978-1-956792-0344 doi: 10.249663ijcci..22277 2023/335. URL http://wwwwjjjjaiiggpnneeegggggp
Taherkhani, A., Belatreche, A., Li, Y., Cosma, G., Maguire, L. P., and McGinnity, T. M. A review of learning in biologically plausible spiking neural networks. Neural Networks, 122:253-277 272, 2020. ISSN 0893-6080. doi: 10.10166j.neumet22019 9.0............ URL https///www.sciene setiestccoosssiiccccmmeeeiiiissp
Lian, S., Shen, J., Wang. Z., and Tang, H. IM-LIF: Improved Neuronal Dynamics With Attention Mech- anism for Direct Training Deep Spiking Neural Net- work. IEEE Trunsactions on Emerging Topics in Com- putational Intelligence, pp. 1-11, 2024. ISSN 2471- 285X. doi: 10. 109/TTTC1.222443335533333 https://iceexplore..e weweeeexpoorreieeeeeoooeeeeeeeee wwwp/abbrrae/cccmume//1/3.333a. Conference Name: IEEE Transactions on Emerging Top- ics in Computational Intelligence.
Turrigiano, G. G. and Nelson, S. B. Homeostatic plas- ticity in the developing nervous system. Nature Re- views Neuroscience, 5(2):97-107, February 2004. ISSN 1471-003X, 1471-0048. doi: 10.1038/ 1327. URL 1327.
URL
Wang, B., Cao, J., Chen, J., Feng, S., and Wang, Y. A New ANN-SNN Conversion Method with High Accuracy, Low Latency and Good Robustness. In Proceedings of IJCAI, pp. 3067-3075, Macau, SAR China, 2023a. ISBN 978- 1-956792-03-4, doi: 10. 000244663jjcaii.2223/444 URL http://www.iccccorrrrreeeetsnsss
Lin, J., Gan, C., and Han, S. TSM: Temporal Shift Module for Efficient Video Understanding. pp. 7083-7093, 2019. URL https///openaccess.
wtt.e/..ooo//ootetttcccc.. 201 (ttp:/LiiinTSMMMTTTmpopo _ShiSttti www.aooouuuoeeefeeeeeooe Video. Understanding ICCV 2019 paper.html.
Wang. Y., Shi, K., Lu, C., Liu, Y., Zhang, M., and Qu, H. Spatial-Temporal Self-Attention for Asynchronous Spiking Neural Networks. In Proceedings of IJCAI, pp. 3085-3093, Macau, SAR China, August 2023b. ISBN 978- 978-1-956792-00-4. doi: 10. 10.249633ijcci 20233334, URL http://www.iiiaaarrrr/mmceeenss
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. Swin Transformer: Hierarchical Vision
Transformer Using Shifted Windows. pp. 10012-10022,
2021. URI. http:://penacees....tc.cooooooee 2021/html/Liu_Swe www.uu.SSS.....tffommmeee.. Hierarchicall n. Transformer. Using Shifted. Windows JCCV 2021 aper.
Wang, Z., She, Q., and Smolic, A. ACTION-Net Multipath Excitation for Action Recognition. pp. 13214-13223,
Neftci, E. O., Mostafa, H., and Zenke, F. Surrogate Gra- dient Learning in Spiking Neural Networks: Bringing the Power of Gradient- Based Optimization to Spiking Neural Networks. IEEE Signal Processing Magazine, 36(6):51-63, November 2019. ISSN 1558-0792. doi: 10.1 D0.100//SPP.201.299311 URL https//iiccexploreii ce..org/abstrac//dcummee////9999 1809. Conference Name: IEEE Signal Processing Magazine.
2021. URL https://opemaccesstheevrt hwwp:pppwwecceeeee.e.../ecc//ena
PR2021/htm//Wang RR200//tt///anggAATTONN...L, ACTION-NTONNNNNCCC Multippth Excitatio n for Action Recognition CVPR 2021 paper.html.
Wu, B., Wan, A., Yue, X., Jin, P., Zhao, S., Golmant, N., Gholaminejad, A., Gonzalez, J., and Keutzer, K. Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions. pp. 9127-9135, 2018a. URL wtw.:/accccsseeeeecoo///e/eeeeee ml/Wu A Zero Zero.CVPR 2018 paper.html.
8/ht
Qiao, N., Mostafa, H., Corradi, F., Osswald, M., Stefanini, F. Sunislawska, D., and Indiveri, G. A reconfigurable on-line learning spiking neuromorphic processor com- prising 256 neurons and 128K synapses. Frontiers in Neuroscience, 9, April 2015. ISSN 1662-453X. doi: 20339//finns201...... 2015.00141. URL http://wwwwfrootieriin
Wu, Y., Deng, L., Li. G., Zhu, J., and Shi, L. Opatio-Tempors Backpropagation for Training High- Performance Spiking Neural Networks. Frontiers in Neu- roscience, 12:323875, 2018h, 8b. ISSN 1662-453X. URL

---

TS-SNN: Temporal Shift Module for Spiking Neural Networks
http://wwww.ffootteeeeee///tttss 00331.
Yu, K., Zhang, T., Wang, H., and Xu, Q. Fsta-snn: Frequency- based spatial-temporal attention module for spiking neural networks. In Proceedings of the AAAI Con- ference on Artificial Intelligence, volume 39, pp. 22227- 22235, 2025b.
Wu, Y., Deng, L., Li, G., Zhu, J., Xie, Y., and Shi, L. Direct Training for Spiking Neural Networks: Faster, Larger, Better. Proceedings of AAAI. 33:1311-1318, July 2019. doi: 1aa1660/aaaai.33311.333001011 oog//inee.ppp/AAAAAaarreeeeeeeee
URL https://o//ssse Number: 01.
Yu, T., Li, X., Cai, Y., Sun, M., and Li, P. S2-MLP: Spatial- Shift MLP Architecture for Vision. pp. 297-306, 2022. URL http://pwwncceeetttcecc///////// WAVV 22/htm//uu.S2-M.PP... 222--ML---Spaiaa-ShiftLM-22 Architecuue or Vision WACV 2022 paper.html.
Xu, Q., Li, Y., Pang. X., Shen, J., Liu, K., Tang, H., and Pan, G. Biologically inspired structure learning with re- verse knowledge distillation for spiking neural networks, April 2023a. URL
Zhang, T., Yu, K., Zhang, J., and Wang, H. Da-lif: Dual adaptive leaky inregrate-and-iie model for deep spiking neural networks. In ICASSP 2025-2025 IEEE Interna- tional Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP), pp. 1-5. IEEE, 2025a.
arXiv:2304.09500 [es].
Xu. Q.. Li. Y.. Shen, J., Liu, J. K., Tang. H., and Pan, G. Constructing Deep Spiking Neural Networks From Artificial Neural Networks With Knowledge Distillation. In Proceedings of CVPR, pp. 7886-7895, June 2023b. URL https://openaccess. wwaeiessssteeetttee. thecvf. wwwpw/fcco//oooccce..eooooocccce com//oottem/CVPPR2 23/html/Xu http:///XuCoossuutttiii Constructing rks From Artificial Neural Networks paper.html.
Zhang, T., Yu, K., Zhong, X., Wang, H., Xu, Q. and Zhang, Q. Staa-snn: Spatial-temporal attention ag- gregator for spiking neural networks. arXiv preprint arXiv:2503. 02689, 2025b.
Seepinni _Nurral Netwo 202
Zheng, H., Wu, Y., Deng, L., Hu, Y., and Li, G. Go- ing Deeper With Sirectl--rraind Larger Spiking Neu- ral Networks. Proceedings of AAAI, 35:1 1062-11070, May 2021. doi: w...0///aaai..3551......... htts://qjs.aaai.org//n htt.aa/www.nnne.ooo//////aaaaaee Number: 12.
Xu, Q. Fang, X., Li, Y., Shen, J., Ma, D., Xu, Y., and Pan, G. Rsnn: Recurrent spiking neural networks for dynamic spatial-temporal information processing. In Pro- ceedings of the 32nd ACM International Conference on Multimedia, pp. 10602-10610, 2024.
URL
Zheng, Y., Yu, Z., Wang, S., and Huang, T. Spike-Based Motion Estimation for Object Tracking Through Bio- Inspired Unsupervised Learning. IEEE Transactions on Image Processing, 32:335-349, 2023. ISSN 1941-0042.
Yamazaki, K. Vo-Ho, V.-K., Bulsara, D., and Le, N. Spik- ing Neural Networks and Their Applications: A Re- view. Brain Sciences, 12(7):863, July 2022. ISSN 2076-3425. doi: (0.3390/brainsei122006666 http://wwwwmmmihh.coo///76222333
doi: 10.1109/T1P.222...02 222.02.....2222. 168. URL https://iecexplore dieee.or/ddccumen/999899999 Conference Name: IEEE Transactions on Image Processing.
URL Number: 7
Publisher: Multidisciplinary Digital Publishing Institute.
Zhou, Z., Zhu, Y., He, C., Wang, Y., Yan, S., Tian, Y., and Yuan, L. Spikformer: When Spiking Neural Network Meets Transformer. In ICLR, September 2022. URL h.
Yao, M., Zhao, G., Zhang, H., Hu, Y., Deng, L., Tian, Y., Xu, B., and Li, G. Attention Spiking Neural Networks. IEEE Transactions Pattern Analysis and Machine In- telligence, 45(8):9393-941) August 2023. ISSN 1939- URL https:
3539. doi: 10.
Yao, X., Li, F., Mo. Z., and Cheng. J. GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neu- ral Networks. Proceedings of NeurlPS, 35:32160-32171, December 2022. URL https//pooeeddiggnneeiiicccaaa er
Yu, K., Yu, C., Zhang, T., Zhao, X., Yang, S., Wang, H., Zhang, Q., and Xu, Q. Temporal separation with entropy regularization for knowledge distillation in spiking neural networks. arXiv preprint arXiv:2503.03144 2025a.
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A. Appendix.
A.1. LIF Modal
Mathematically, a LIF neuron can be represented as follows:
du(t) dt
(u(l) Mrees + I(),
(8)
where r denotes the membrane time constant, u(() presents the membrane potential at the moment i, Urest is the resting potential of the neuron, and (t) is pre-synaptic input at moment.
A.2. Datasets Details and Augmentations
CIFAR-10. CIFAR-10 (Krizhevsky et al., 2010) is a widely recognized benchmark dataset for image classification tasks. consists of 50,000 training images and 10,000 test images, all sized at 32 x 32. The dataset contains 10 distinct classes, representing common objects such as airplanes, cars, birds, and cats. CIFAR-10 serves as standard for evaluating the performance of image classification algorithms,, offering diverse set of visual challenges. In our approach, we apply data augmentation techniques including cropping, horizontal flipping, and cutout. Additionally, during training, introduce random augmentations by selecting two strategies from contrast enhancement, rotation, and translation. These augmentations enhance the model's 's robustness and its ability to generalize across various visual scenarios.
CIFAR-100. CIFAR-100 (Krizhevsky et al., 2010) is an extension of the CIFAR-I0 dataset, designed for more fine-grained classification tasks. comprises 50,000 training images and 10,000 test images, all with dimensions of 32 x 32. The dataset features 100 classes, each belonging to one of 20 superclasses, making a more challenging benchmark compared to CIFAR-10 10. The data augmentation strategy used for CIFAR-100 100 is consistent with that applied to CIFAR- 10, providing robust evaluation framework for models across broader range of object categories.
ImageNet. ImageNet (Deng et al., 2009) is a large-scale image dataset extensively used for benchmarking image classification algorithms. It includes 1.3 million training images across 1,000 categories, along with 50,000 validation images. These classes represent a wide range of objects, including animals, vehicles, and everyday items. Compared to CIFAR-10 and CIFAR-100, ImageNet presents larger and more complex collection of images, offering more robust benchmark for evaluating model performance. In our experiments, in (He et al., 2016). Images are randomly cropped from either their original version or horizontally flipped version to size of 224 x 224 pixels, followed by data normalization. For testing samples, images are resized to 224 x 224 pixels and subject to center cropping, after which data normalization is also applied.
utilize the data augmentation techniques outlined
CIFARIO-DVS. CIFARIO-DV'S (Li et al., 2017a) is an event-based version of the CIFAR-10 dataset, captured using dynamic vision sensor (DVS) camera. This dataset consists of 10,000 event streams, each sized 128 x 128, derived from the original CIFAR-10 images. The 10 classes in CIFAR10-DVS each contain 1,000 samples, and the dataset is split into training and testing sets with a 9:1 ratio. During preprocessing, random horizontal flips (with probability of 0.5) are applied, followed by random selection from augmentations such as rolling, rotation, cutout, and shear. These augmentations enhance the variability and robustness of the dataset(Guan & Zhao, 2022; Guan et al., 2020).
A.3. Experimental Setups
All code implementations were based on the Py Torch framework. The experiments were conducted on a single RTX 3090 GPU for all datasets except ImageNet, which was trained using a configuration of eight RTX 4090 GPUs. In all experiments, the SGD optimizer with a momentum of 0.9 was used, along with the Conealnn learning rate adjustment strategy.
CIFAR-10/100, For the CIFAR-I0 and CIFAR-100 datasets, the initial learning rate set to 0. with batch size of 128 and the number of training epochs set to 500. The parameter was initialized at 0.5 and C2. was set at 32.
CIFARIO-DVS. For the CIFARIO-DVS dataset, the initial learning rate was set to 0.1, with a batch size of 32 and 300 training epochs. The parameter Cs was set 32, and a was initialized at 0.2.
13

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TS-SNN: Temporal Shift Module for Spiking Neural Networks
Firing Rate of Each Layer in Vanilla SNN and TS SNN
0.00
Varilo SKN
SMM
Reiig Rate
0.40
0.35
11 Layer
10
12
19
Figure 7. Average Firing Rate of each layer in Vanilla SNN and TS SNN
ImageNet. For the ImageNet dataset, the initial learning training epochs. The parameter C was set 32, and was initialized 0.2.
0.1, with a batch size of 64 and total of 320
A.4. Analysis of Computation Efficiency
In ANNs, each operation involves multiplication and accumulation (MAC) process. The total number of MAC operations (#MAC) in ANN can be calculated directly and remains constant for given network structure. In contrast, spiking neural networks (SNNs) perform only accumulation computation (AC) per operation, which occurs when incoming spike is received. The number of AC operations can be estimated by taking the layer-wise product and sum of the average spike activities, in relation to the number of synaptic connections.
#MAC = Σ (MMA))
(9)
#AC= 1C#MACC x 01) x
(10)
Here, 04 represents the average spiking activity for layer 1. The first, rate-encoding layer of an SNN does not benefit from multiplication-fret operations and therefore involves MACs, while the subsequent layers rely on ACs for computation. The energy consumption E for both ANN and SNN, accounting MACs and ACs across all network layers, is given by: ESNN #MAC, EMAC + #AC x EAC and EANN #MAC EMAC.
Based previous studies in SNN research (Yao et al., 2023; Chakraborty et al., 2021), assume that the operations implemented using 32-bit floating-point (FL. 45 nm 0.9V chip (Horowitz, 2014), where a MAC operation consumes 4.6 pJ and an AC operation consumes 0.9 pJ. This comparison suggests that one synaptic operation in an ANN is roughly equivalent to five synaptic operations in an SNN. It is important to note that this estimation is conservative, and the energy consumption of SNNs on specialized hardware designs can be significantly lower, potentially reduced by up to 12x to 77 fJ per synaptic operation (SOP) (Qiao et al., 2015).
A.5. Analysis of Firing Rates
Firing rates play a crucial role in biological neural networks, where they are used to encode information, forming the basis of neural communication and signal processing. In computational models like SNNs, the firing rate a key metric for simulating and analyzing neuronal dynamies, providing insights into the role of neurons in complex behaviors and information processing tasks. The firing rate quantifies the activity level of neuron over specified period, serving vital indicator of the neuron's 's responsiveness to input stimuli-higher firing rates typically signify stronger responses.
In our analysis, we compare the firing rates of the proposed TS-SNN with a vanilla SNN model, as summarized in Figure 7. Firing rates were recorded across four timesteps, with the average firing rate for each layer calculated based on these timesteps. This comparison highlights the differences in neuronal activity between the two models, providing insights into their respective processing efficiencies.
14

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TS-SNN: Temporal Shift Module for Spiking Neural Networks
In the context of the overall network, the spike firing rate of the vanilla SNN is 33.06%, while that of the TS-SNN is 35.61%. This suggests that the TS module enhances model performance without substantially increasing spike output, thereby preserving the inherent sparsity of the original SNN.
15


Reviews:
[{"review_id": "SPMcAwcpXF", "reviewer": "Reviewer_bUca", "summary": "Research related to spiking neural networks (SNNs) has received increasing attention, but it is still a challenge to strike a balance between time steps and low energy consumption. In this paper, we introduce the Time Shift Module for Spiking Neural Networks (TS-SNN), which integrates past, present, and future spike features within a single time step through a simple but effective shift operation. The residual combination method prevents information loss by integrating shifted and original features. The model achieves optimal performance on multiple datasets.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for this insightful question. Indeed, one of the advantages of the Temporal Shift module is its lightweight and general design, making it applicable beyond classification.\n\nWhile our current work focuses on image classification due to space constraints and the need for standardized comparisons with prior SNN models, we agree that the TS module has strong potential in other tasks such as object detection, semantic segmentation, or event-based video understanding.\n\nIn future work, we plan to extend TS-SNN to such domains. Additionally, the TS module is designed to be plug-and-play and architecture-agnostic, so it can be seamlessly integrated into models for these tasks with minimal modifications.\n\nWe appreciate the suggestion and will include a discussion of this in the camera-ready version.", "claims_and_evidence": "Yes, the claims made in the submission are supported by clear and convincing evidence.", "methods_and_evaluation_criteria": "The time-shift module moves some channels forward by a +1 operation and others backward by a -1 operation, while the rest of the channels remain unchanged along the time dimension. By integrating the spike features of different time steps, TS-SNN effectively mitigates the effect of the loss of the information of the initial time step and solves the problem of the insufficient feature extraction of the end time step. The proposed method can effectively reduce the forgetting of past time-step information, realize the learning of future time-steps, and establish robust long-term temporal dependencies.", "theoretical_claims": "Not applicable. This paper does not involve complex theoretical proofs.", "experimental_designs_or_analyses": "The authors have experimentally validated the proposed method in terms of both model performance and energy consumption analysis, and the results demonstrate optimal performance and lower power consumption.", "relation_to_broader_scientific_literature": "As a plug-and-play module, the TS module has great potential for a wide range of applications", "essential_references_not_discussed": "I think the paper has cited enough relevant literatures.", "other_strengths_and_weaknesses": "None", "other_comments_or_suggestions": "None", "questions_for_authors": "Due to the lightweight nature of the TS module, it has the potential to be widely used in other tasks besides classification tasks as well, can the authors give some validation?", "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": "l2tiPVTffA", "reviewer": "Reviewer_cLUM", "summary": "This paper introduce a Temporal Shift module for SNN called TS-SNN, which enhances the ability of SNNs for temporal information. The TS-SNN consists of two parts. The first part, Temporal Shift module (TS), divides the spike output matrix into C_k groups in the channel dimension, and divides each group into three parts: left shift, right shift, unchanged shift in the time dimension, respectively. The second parts is a residual connection that adds the shifted spike matrix to the original spike matrix according to a ratio $\\alpha$. The authors conducted ablation experiments to find best parameters setting of the TS module. And the comparative experimental results show that TS-SNN achieves excellent results on both static datasets and neuromorphic datasets.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review and constructive feedback. We address your points as follows:\n\n---\n## Questions for Authors\n\n**How TS operation when timestep = 1**\n\nAs you correctly observed, the performance of our TS-SNN with ResNet-19 on CIFAR-100 under a single timestep lags slightly behind MPBN, whereas it outperforms MPBN on CIFAR-10. This behavior can be attributed to the nature of the **TS operation** when T=1. In this case, there are no past or future information to reference, and the shift operation defaults to zero-padding, leaving only the residual connection  $Z' = \\alpha Z + X$ active. Although this residual fusion can subtly affect feature extraction, it does not consistently enhance model stability. This observation demonstrates that the primary advantage of our method arises from the effective feature fusion brought by the temporal shift operations across multiple timesteps.\nWe will include this clarification in the camera-ready version of the paper.\n\n---\n## Weakness\n\n### 1. Theoretical Analysis and Experimental Verification\n\nWe appreciate your observation.\nIn the camera-ready version, we will incorporate theoretical insights that will substantially strengthen the paper, including:\n\n- **Expansion of the Temporal Receptive Field**  \nBy shifting features forward and backward, the TS module enables neurons to access information from adjacent timesteps, which is effectively allowing each neuron to \"see\" more temporal context. This operation expands the temporal receptive field from \\(d\\) to \\(2d + 1\\) without incurring extra parameters or computational cost.\n\n- **Increase in Mutual Information and Entropy**  \nThe TS module fuses inputs ${X_{t-1}, X_t, X_{t+1}}$ to compute $S_t$, thereby increasing its temporal entropy $H(S_t)$. Under stationary input conditions, the mutual information $I(S_t; X_{t-1:t+1})$ exceeds $I(S_t; X_t)$, demonstrating enhanced temporal context modeling.\n\n---\n### 2. Implementation on Neuromorphic Hardware\n\nYour concern that the TS module may incur additional memory costs on neuromorphic chips is reasonable. However, relative to the significant energy efficiency gains demonstrated by our approach, the extra storage requirement is minimal. The hardware optimization strategies will be promising in future work to further mitigate this cost in practical deployments.\n\n---\n### 3. Setting of the $\\alpha$ Parameter\n\nWe appreciate your insightful observation regarding the $\\alpha$ parameter. In our implementation details, we mistakenly stated that the initial value of $\\alpha$ is 0.5 for all datasets. The correct settings, as detailed in the supplementary material, are as follows: for CIFAR-10 and CIFAR-100, the initial $\\alpha$ is 0.5; for ImageNet and CIFAR10-DVS, the initial $\\alpha$ is 0.2. We will correct this error in the camera-ready version.\n\nFurthermore, we specified in the manuscript that the experimental range for$\\alpha$ is between 0.2 and 0.5. Our experiments validated the effectiveness of this range across different datasets. We found that the initial value of $\\alpha$ is critical—if it is set above 0.5, training tends to collapse. Therefore, we recommend tuning $\\alpha$ as a hyperparameter within the 0.2–0.5 range based on the specific dataset.\n\n---\n### 4. Training Epochs and Convergence\n\nAlthough our experiments on CIFAR-10 and CIFAR-100 used 500 epochs, we observed that competitive performance can be achieved with as few as 200 epochs—the performance difference is within 0.4% as shown in the table below.\nWe chose 500 epochs to align with some current SOTA settings and to ensure robust convergence. \nIn the camera-ready version, we will include this experiments to clarify this.\n\n| Archi | T | CIFAR10 200 | CIFAR10 500 | ↑200→500 | ↑200→BL | ↑500→BL | CIFAR100 200 | CIFAR100 500 | ↑200→500 | ↑200→BL | ↑500→BL |\n|-|-|-|--|--|--|-|-|-|-|-|-|\n| | |\\% |\\%|\\%|\\%|\\%|\\%|\\%|\\%|\\%|\\%|\n| R-19 | 1 | 96.27|96.50|.23 | .21| .44| 78.24 |78.61|.37 | -.47| -.10 |\n| R-19 | 2 | 96.54|96.72|.18 | .07| .25 | 79.99|80.28|.29 | .48| .77|\n| R-20 | 1 | 92.68|93.03|.35 | .46| .81| 68.90 |69.02|.12 | .49| .61|\n| R-20 | 2 | 93.99|94.11|.12 | .45| .57| 71.58|71.83|.25 | .79| 1.04|\n| R-20 | 4 | 94.39|94.71|.32 | .11| .43| 73.07|73.46|.39 | .77| 1.16|\n\n---\n## Other Comments and Suggestions\n\nThank you for your suggestion regarding Figures 5 and 6. In the camera-ready revision, we will update these figures to include baseline data (i.e., models without the TS module), clearly demonstrating the performance improvements attributable solely to the TS module.\n\n---\nWe greatly appreciate your valuable advice, which will help enhance the quality and clarity of our work.", "claims_and_evidence": "The authors claim that the TS module can improve the ability of SNN to extract temporal information, but they have not theoretically or experimentally demonstrated their claim.", "methods_and_evaluation_criteria": "The TS module, if used as a training method, can improve the robustness of SNNs to the noise (spike shift or spike loss) to a certain extent. This method has a certain influence in the SNN field.", "theoretical_claims": "The authors did not provide theoretical proof for their methods.", "experimental_designs_or_analyses": "Yes, I have checked the author's experimental design, and the experiment is relatively reasonable. However, the authors used larger training epochs, which makes me doubt whether the their method will cause slow training convergence.", "relation_to_broader_scientific_literature": "NA", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "Strength:\n1. The method proposed in this paper is simple and effective, achieving SOTA performance on both static datasets and neuromorphic datasets. \n2. The writing of the paper is concise and clear.\n\nWeakness:\n1. The authors claims that TS-SNN can enhance the ability of SNN to extract temporal information, but the paper lacks relevant theoretical analysis and experimental verification as to why it is effective.\n2. The TS method requires information about the spike output at previous and subsequent moments. However, on the neuromorphic chips, it difficult to obtain the spike information at different moments (requires additional memory and cost to record the spikes).\n3. The setting of $\\alpha$ is different for different datasets, and the paper does not provide the setting rule for $\\alpha$, which may reduce the practicality of the method.\n4. The authors used more training epochs in the experiment part, exceeding the common settings in other SNN works.", "other_comments_or_suggestions": "Figures 5 and 6 should provide the baseline without TS-SNN method.", "questions_for_authors": "In Table 1, the author demonstrates the results under timestep is 1, where the SNN does not use any temporal information. In this situation, how does the TS method operation, and does the TS method improve training performance?", "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": "dzHm1hgSfd", "reviewer": "Reviewer_x8Ri", "summary": "The paper introduces the Temporal Shift module for Spiking Neural Networks, by utilizing a Temporal Shift operation, the model integrates past, present, and future spike features within a single timestep, aiming to improve the temporal dynamics of SNNs. The key advantage of TS-SNN lies in its ability to model temporal dependencies efficiently, enhancing performance with minimal increasing computational costs. Experimental results across multiple benchmark datasets demonstrate that TS-SNN outperforms SOTA models, achieving high accuracy with fewer timesteps. The approach is energy-efficient, aligning well with the goals of neuromorphic computing.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Questions for Authors\n\n**Performance Discrepancy for ResNet-19 on CIFAR-100 (Timestep=1)**\n\nThank you for pointing out this discrepancy. As you correctly observed, the performance of our TS-SNN with ResNet-19 on CIFAR-100 under a single timestep lags slightly behind MPBN, whereas it outperforms MPBN on CIFAR-10. This behavior can be attributed to the nature of the **Temporal Shift (TS) operation** when T=1. In this case, there are no past or future information to reference, and the shift operation defaults to zero-padding. The resulting update becomes a residual connection:\n\n$Z' = \\alpha Z + X$.\n\nAs a result, the TS operation brings limited benefit when T=1, and the model's performance largely relies on the backbone architecture and input statistics. \nNotably, CIFAR-100 is a more challenging dataset with fewer samples per class, which amplifies the limitations of reduced temporal modeling under T=1.\nThis observation demonstrates that the primary advantage of our method arises from the effective feature fusion brought by the temporal shift operations across multiple timesteps.\nWe will include this clarification in the camera-ready version of the paper.\n\n---\n## Weakness\n\n### 1. Lack of Discussion on Limitations\n\nWe appreciate your suggestion. We will include two primary limitations of our method:\n\n- **Restricted Effectiveness at Low Timesteps**  \n  As mentioned above, when T=1, the TS module cannot utilize temporal context, limiting its effectiveness. The model reverts to near-baseline behavior, and performance gains are minimal or inconsistent.\n\n- **Memory Requirements**  \n  The TS operation assumes access to spike outputs from both past and future timesteps within a local window. On neuromorphic hardware, this may require additional memory and access latency to buffer or store spike states across timesteps. While this overhead is generally modest compared to the savings in computation and energy (since spike buffers are lightweight), it could become a bottleneck for highly constrained or streaming systems.\nWe plan to explicitly discuss these limitations and potential mitigations in the camera-ready version.\n\n---\n\n### 2. Performance Discrepancy in Table 1\n\nPlease see our response above under ##Questions for Authors## for a detailed explanation.\n\n---\n\n## Other Comments and Suggestions\n\n**Formatting Issues**\n\nWe appreciate your feedback on formatting. We have revised the LaTeX alignment of all equations to ensure consistency—either center alignment or equation numbering, depending on context. This significantly improves the visual quality and readability of the manuscript.\n\n---\nWe sincerely thank the reviewer for their thoughtful and helpful suggestions. We will incorporate all the suggested changes into the camera-ready version of the paper.", "claims_and_evidence": "The claims regarding the efficiency and performance of the TS-SNN method are well-supported by experimental evidence with convincing comparisons against existing methods on benchmark datasets, demonstrating superior performance in terms of accuracy and energy consumption.", "methods_and_evaluation_criteria": "Yes. The Temporal Shift module is well-suited for enhancing the temporal dynamics of SNNs, and the benchmark datasets used are standard for evaluating image classification and event-based vision tasks. The use of CIFAR-10, CIFAR-100, ImageNet, and CIFAR10-DVS datasets ensures that the model is tested under varied conditions, demonstrating its robustness across different types of data.", "theoretical_claims": "The theoretical claims regarding the TS module’s ability to efficiently integrate Spatio-temporal features through shifting operations are clearly explained. There are no formal proofs provided for these claims, but approach is based on well-understood principles in deep learning and features of SNNs and the experimental results substantiate the theory.", "experimental_designs_or_analyses": "The experimental design is sound. The authors conduct extensive ablation studies to analyze the effectiveness of various components of the TS-SNN, such as the channel folding factor and the temporal shift strategy. The results are robust and show the effectiveness of the method across different architectures. The experiments on energy consumption are particularly valuable, highlighting the energy efficiency of the approach compared to other models.", "relation_to_broader_scientific_literature": "As the authors mentioned, the paper builds on existing work in temporal modeling and relates their approach to earlier methods such as the Temporal Shift Module and 3D CNNs. This paper introduces the Temporal Shift to SNNs, highlighting the novelty of incorporating spatial-temporal features into SNNs. The comparison with SOTA methods further demonstrates the significance of this contribution.", "essential_references_not_discussed": "While the paper does a good job of citing important related works, some recent developments in spiking neural networks and temporal modeling might be relevant but aren’t discussed in detail. For example, a more thorough review of the intersection between SNNs and transformer architectures could have been beneficial.", "other_strengths_and_weaknesses": "The paper’s strength lies in its originality, particularly in the introduction of the Temporal Shift module for SNNs, which enhances temporal modeling with minimal computational overhead. The experimental results are compelling, demonstrating the ability of the TS-SNN to outperform existing models with fewer timesteps. However, a more thorough discussion of potential limitations (e.g., situations where the temporal shift might be less effective) would provide a more balanced perspective. Additionally, in Tab. 1, the performance of ResNet-19 with a timestep of 1 on CIFAR-100 is lower than MPBN, while it outperforms MPBN on CIFAR-10. The paper does not provide a clear explanation for this discrepancy.", "other_comments_or_suggestions": "Formatting improvement: some equations could be better aligned.", "questions_for_authors": "Could the authors provide an explanation for the lower performance of ResNet-19 on CIFAR-100 compared to MPBN when the timestep is set to 1, as shown in Table 1?", "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:

Meta-review: This paper proposes TS-SNN, a lightweight and effective method to enhance temporal feature extraction in Spiking Neural Networks (SNNs) via a novel Temporal Shift module. The module shifts spike features across time and integrates them with a residual connection, significantly improving the temporal receptive field while maintaining low computational and memory overhead.

### **Strengths**
- **Technical novelty and simplicity**: The proposed TS module is architecture-agnostic, lightweight (introducing only one learnable parameter), and easy to integrate, making it a practical contribution for the SNN community.
- **Strong empirical results**: The method demonstrates state-of-the-art performance on several benchmark datasets, including CIFAR-10, CIFAR-100, ImageNet, and CIFAR10-DVS. It also maintains energy efficiency with fewer timesteps, aligning well with the goals of neuromorphic computing.
- **Thorough experimentation**: The paper includes ablation studies and comparisons with strong baselines. Reviewers appreciated the experimental rigor, including supplementary analysis on energy consumption and hyperparameter sensitivity.
- **Clarity and writing**: The manuscript is well-written, and the authors’ rebuttals are detailed and constructive. They committed to incorporating several improvements in the camera-ready version, including additional theoretical explanations, clearer experimental settings, and better formatting.

### **Weaknesses**
- **Lack of theoretical depth**: While the authors provided some theoretical insights in the rebuttal (e.g., expanded temporal receptive field, mutual information increase), the initial manuscript lacked formal theoretical analysis or verification experiments to support the core claim that temporal shifting improves gradient flow or learning dynamics in SNNs.
- **Hardware realism**: Reviewers raised valid concerns about the feasibility of implementing the temporal shift operation on neuromorphic hardware due to the need for spike access across timesteps, which may incur memory and latency costs. Although the authors acknowledged this and argued the overhead is modest, the issue remains relevant for real-world deployment.
- **Hyperparameter generality**: The method requires tuning the residual fusion ratio (α), and the appropriate setting differs by dataset. The paper did not provide a principled guideline for selecting this parameter, which could impact reproducibility and generalization.
- **Limited effectiveness at T=1**: Reviewers noted and the authors confirmed that the TS module has little benefit when the timestep is set to 1, as the operation degenerates into residual fusion with zero-padding. This limits the method’s applicability in extremely low-timestep regimes.

### **Rebuttal Assessment**
The authors responded thoroughly to all reviewer concerns. They clarified the working mechanism of TS at T=1, provided additional experimental results showing performance is not overly dependent on training epochs, and outlined theoretical intuitions for the method’s effectiveness. Their planned additions (e.g., discussion of limitations, parameter clarification, formatting improvements) will significantly strengthen the final version.
Decision: Accept (poster)