Spiking neural networks (SNNs) offer great potential for low-power intelligent computing owing to their event-driven nature, yet their deployment is limited by high inference latency and inefficient hardware utilization caused by temporal dependencies. Existing accelerators struggle to balance timesteps, accuracy, and energy efficiency, while hardware-oriented timestep compression remains underexplored. To address these challenges, this work presents STISA, a unified algorithm-hardware co-design framework featuring: 1) a Temporal Splitter Compression (TSC) technique with a Temporal Split Structure (TSS) to reduce temporal redundancy while preserving network dynamics; 2)a hardware-efficient spatiotemporal parallelism scheme, which co-optimizes dataflow and architecture through a Temporal-Prioritized Output-Stationary (TPOS) mapping and a flexible Block-Mux Streaming (BMS) architecture; and 3) a joint TSC-TPOS-BMS co-optimization framework featuring a bandwidth-aware resource allocation strategy to achieve balanced accuracy, latency, and energy efficiency across diverse network architectures. On the algorithmic side, TSC reduces synaptic operations by 24.44%-51.71% while achieving competitive accuracies of 96.38%, 81.10%, 68.51%, and 82.40% on CIFAR-10, CIFAR-100, ImageNet, and DVS-CIFAR10, respectively. On the hardware side, FPGA prototypes deliver up to$7.99\times $speedup, 30.4% power reduction, and 30.1% LUT reduction. Compared with state-of-the-art SNN accelerators, STISA achieves 0.16 GOPS/W/PE, demonstrating its scalability and superior energy efficiency for real-time SNN inference under constrained hardware resources.

Spiking Neural Networks (SNNs) have garnered considerable attention as a potential alternative to Artificial Neural Networks (ANNs). Recent studies have highlighted SNNs’ potential on large-scale datasets. For SNN training, two main approaches exist: direct training and ANN-to-SNN (ANN2SNN) conversion. To fully leverage existing ANN models in guiding SNN learning, either direct ANN-to-SNN conversion or ANN-SNN distillation training can be employed. In this paper, we propose an ANN-SNN distillation framework from the ANN-to-SNN perspective, designed with a block-wise replacement strategy for ANN-guided learning. By generating intermediate hybrid models that progressively align SNN feature spaces to those of ANN through rate-based features, our framework naturally incorporates rate-based backpropagation as a training method. Our approach achieves results comparable to or better than state-of-the-art SNN distillation methods, showing both training and learning efficiency.

Spiking Neural Networks (SNNs), inspired by the human brain, offer significant computational efficiency through discrete spike-based information transfer. Despite their potential to reduce inference energy consumption, a performance gap persists between SNNs and Artificial Neural Networks (ANNs), primarily due to current training methods and inherent model limitations. While recent research has aimed to enhance SNN learning by employing knowledge distillation (KD) from ANN teacher networks, traditional distillation techniques often overlook the distinctive spatiotemporal properties of SNNs, thus failing to fully leverage their advantages. To overcome these challenge, we propose a novel logit distillation method characterized by temporal separation and entropy regularization. This approach improves existing SNN distillation techniques by performing distillation learning on logits across different time steps, rather than merely on aggregated output features. Furthermore, the integration of entropy regularization stabilizes model optimization and further boosts the performance. Extensive experimental results indicate that our method surpasses prior SNN distillation strategies, whether based on logit distillation, feature distillation, or a combination of both. Our project is available at https://github.com/yukairong/TSER.

Spiking Neural Networks (SNNs) represent a promising avenue for energy-efficient neuromorphic computing. Despite their potential, SNNs typically underperform compared to Artificial Neural Networks (ANNs) due to their complex spatio-temporal dynamics. To improve learning in these networks, researchers have developed various approaches that account for their unique characteristics—among them, normalization techniques have proven especially important. Recently, online learning algorithms have been explored for SNN training as they update network weights using only temporally local information, avoiding the high memory demands associated with Backpropagation Through Time (BPTT). However, the computational mechanism of online learning, which relies on temporally local information to update weights, hinders the application of integrating effective normalization techniques tailored for SNNs. In this work, we propose a Time-based Statistics Estimation (TSE) method to address limitations in existing normalization strategies for SNNs. We begin by establishing a systematic link between overall statistics and time-step-specific ones, leveraging the decomposability of key statistical measures. This insight allows our proposed TSE method to reliably estimate overall statistics using only recent iterations. Furthermore, the proposed method is compatible with both BPTT and online learning, consistently yielding strong performance across learning paradigms. Experiments on CIFAR-10, CIFAR-100, ImageNet, and DVS-CIFAR10 datasets demonstrate the superior performance of our method on both static and neuromorphic datasets. In particular, our method achieves state-of-the-art performance in online learning for SNN training.

Spiking Neural Networks (SNNs) are emerging as a brain-inspired alternative to traditional Artificial Neural Networks (ANNs), prized for their potential energy efficiency on neuromorphic hardware. Despite this, SNNs often suffer from accuracy degradation compared to ANNs and face deployment challenges due to fixed inference timesteps, which require retraining for adjustments, limiting operational flexibility. To address these issues, our work considers the spatio-temporal property inherent in SNNs, and proposes a novel distillation framework for deep SNNs that optimizes performance across full-range timesteps without specific retraining, enhancing both efficacy and deployment adaptability. We provide both theoretical analysis and empirical validations to illustrate that training guarantees the convergence of all implicit models across full-range timesteps. Experimental results on CIFAR-10, CIFAR-100, CIFAR10-DVS, and ImageNet demonstrate state-of-the-art performance among distillation-based SNNs training methods. Our code is available at https://github.com/Intelli-Chip-Lab/snn_temporal_decoupling_distillation.

Spiking Neural Networks (SNNs) exhibit exceptional energy efficiency on neuromorphic hardware due to their sparse activation patterns. However, conventional training methods based on surrogate gradients and Backpropagation Through Time (BPTT) not only lag behind Artificial Neural Networks (ANNs) in performance, but also incur significant computational and memory overheads that grow linearly with the temporal dimension. To enable high-performance SNN training under limited computational resources, we propose an enhanced self-distillation framework, jointly optimized with rate-based backpropagation. Specifically, the firing rates of intermediate SNN layers are projected onto lightweight ANN branches, and high-quality knowledge generated by the model itself is used to optimize substructures through the ANN pathways. Unlike traditional self-distillation paradigms, we observe that low-quality self-generated knowledge may hinder convergence. To address this, we decouple the teacher signal into reliable and unreliable components, ensuring that only reliable knowledge is used to guide the optimization of the model. Extensive experiments on CIFAR-10, CIFAR-100, CIFAR10-DVS, and ImageNet demonstrate that our method reduces training complexity while achieving high-performance SNN training. Our code is available at https://github.com/Intelli-Chip-Lab/enhanced-self-distillation-framework-for-snn.

Recent insights have revealed that rate-coding is a primary form of information representation captured by surrogate-gradient-based Backpropagation Through Time (BPTT) in training deep Spiking Neural Networks (SNNs). Motivated by these findings, we propose rate-based backpropagation, a training strategy specifically designed to exploit rate-based representations to reduce the complexity of BPTT. Our method minimizes reliance on detailed temporal derivatives by focusing on averaged dynamics, streamlining the computational graph to reduce memory and computational demands of SNNs training. We substantiate the rationality of the gradient approximation between BPTT and the proposed method through both theoretical analysis and empirical observations. Comprehensive experiments on CIFAR-10, CIFAR-100, ImageNet, and CIFAR10-DVS validate that our method achieves comparable performance to BPTT counterparts, and surpasses state-of-the-art efficient training techniques. By leveraging the inherent benefits of rate-coding, this work sets the stage for more scalable and efficient SNNs training within resource-constrained environments.