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Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks

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arxiv 2303.04347 v1 pith:7N3E5MIH submitted 2023-03-08 cs.NE

Tong Bu , Wei Fang , Jianhao Ding , PengLin Dai , Zhaofei Yu , Tiejun Huang This is my paper
classification cs.NE
keywords snnsconversionactivationann-snnannsfunctiontime-stepsdatasets
verification ladder T0 review T1 audit T2 compute T3 formal
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Spiking Neural Networks (SNNs) have gained great attraction due to their distinctive properties of low power consumption and fast inference on neuromorphic hardware. As the most effective method to get deep SNNs, ANN-SNN conversion has achieved comparable performance as ANNs on large-scale datasets. Despite this, it requires long time-steps to match the firing rates of SNNs to the activation of ANNs. As a result, the converted SNN suffers severe performance degradation problems with short time-steps, which hamper the practical application of SNNs. In this paper, we theoretically analyze ANN-SNN conversion error and derive the estimated activation function of SNNs. Then we propose the quantization clip-floor-shift activation function to replace the ReLU activation function in source ANNs, which can better approximate the activation function of SNNs. We prove that the expected conversion error between SNNs and ANNs is zero, enabling us to achieve high-accuracy and ultra-low-latency SNNs. We evaluate our method on CIFAR-10/100 and ImageNet datasets, and show that it outperforms the state-of-the-art ANN-SNN and directly trained SNNs in both accuracy and time-steps. To the best of our knowledge, this is the first time to explore high-performance ANN-SNN conversion with ultra-low latency (4 time-steps). Code is available at https://github.com/putshua/SNN\_conversion\_QCFS

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning

    cs.LG 2026-08 conditional novelty 6.0 of 10

    AS-FedBridge trains a shared pseudo-spike bridge so ANN and SNN clients in federated learning can align representations and improve collaborative accuracy.

  2. Error Amplification Limits ANN-to-SNN Conversion in Continuous Control

    cs.NE 2026-01 conditional novelty 6.0 of 10

    Temporally correlated action errors, amplified by closed-loop dynamics, explain ANN-to-SNN conversion failures in continuous control, and cross-step residual potential initialization mitigates them.

  3. FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A Forward-Forward training framework that freezes spiking layers as black-box encoders and allocates channels by inter-class difficulty achieves 99.58% on MNIST, 92.13% on Fashion-MNIST, and 75.64% on CIFAR-10, the be...

  4. SPACT18: Spiking Human Action Recognition Benchmark Dataset with Complementary RGB and Thermal Modalities

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SPACT18 is claimed to be the first action recognition dataset captured with a spike camera, paired with synchronized RGB and thermal video.

  5. Efficient and Robust Spiking Neural Networks for sEMG-Based Muscle Fatigue Detection

    cs.NE 2026-07 conditional novelty 5.0 of 10

    3- and 4-bit quantized SNNs with SDH training match ANN F1 on two sEMG fatigue datasets, stay more stable under seven noise types, and reduce estimated energy up to 201.77×.

  6. Latency Coding for Efficient and Low-Latency Deep Spiking Neural Networks

    cs.NE 2026-03 conditional novelty 5.0 of 10

    Deep TTFS/latency-coded SNNs can be trained directly with backpropagation, reaching ~93.6% on CIFAR-10 with an average inference latency near one timestep.

  7. SDSNN: A Single-Timestep Spiking Neural Network with Self-Dropping Neuron and Bayesian Optimization

    cs.NE 2025-08 reject novelty 5.0 of 10

    A network using decay-triggered Self-Dropping neurons and Bayesian per-layer time-step search reports Fashion-MNIST 93.72%, CIFAR-10 92.20%, and CIFAR-100 69.45% accuracy with 21-56% energy savings versus multi-timestep LIF.

  8. STI-SNN: A 0.14 GOPS/W/PE Single-Timestep Inference FPGA-based SNN Accelerator with Algorithm and Hardware Co-Design

    cs.AR 2025-06 conditional novelty 5.0 of 10

    A single-timestep SNN inference accelerator on FPGA, co-designed with TET-based temporal pruning, reports competitive accuracy with lower latency and energy than two-timestep implementations.

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