# T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding

arXiv: https://arxiv.org/abs/2003.11741 · HTML: https://mlsanity.com/paper/2003.11741/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2020.

## Built on by (most cited first, 30 of 46)

- [GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks](https://mlsanity.com/paper/2210.13768/) (2022)
- [Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation](https://mlsanity.com/paper/2110.07742/) (2021)
- [Neural Architecture Search for Spiking Neural Networks](https://mlsanity.com/paper/2201.10355/) (2022)
- [Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies](https://mlsanity.com/paper/2304.12760/) (2023)
- [AutoSNN: Towards Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2201.12738/) (2022)
- [Real Spike: Learning Real-valued Spikes for Spiking Neural Networks](https://mlsanity.com/paper/2210.06686/) (2022)
- [Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks](https://mlsanity.com/paper/2104.12528/) (2021)
- [ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories](https://mlsanity.com/paper/2108.10271/) (2021)
- [One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency](https://mlsanity.com/paper/2110.05929/) (2021)
- [Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation](https://mlsanity.com/paper/2207.02702/) (2022)
- [Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence](https://mlsanity.com/paper/2303.10276/) (2023)
- [Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks](https://mlsanity.com/paper/2401.04486/) (2024)
- [An Exact Mapping From ReLU Networks to Spiking Neural Networks](https://mlsanity.com/paper/2212.12522/) (2022)
- [Energy-efficient Knowledge Distillation for Spiking Neural Networks](https://mlsanity.com/paper/2106.07172/) (2021)
- [Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?](https://mlsanity.com/paper/2112.12133/) (2021)
- [Spiking-GAN: A Spiking Generative Adversarial Network Using Time-To-First-Spike Coding](https://mlsanity.com/paper/2106.15420/) (2021)
- [Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16235/) (2023)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks](https://mlsanity.com/paper/2308.10187/) (2023)
- [EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems](https://mlsanity.com/paper/2304.04039/) (2023)
- [RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults](https://mlsanity.com/paper/2304.04041/) (2023)
- [Noise-Robust Deep Spiking Neural Networks with Temporal Information](https://mlsanity.com/paper/2104.11169/) (2021)
- [A Time-to-first-spike Coding and Conversion Aware Training for Energy-Efficient Deep Spiking Neural Network Processor Design](https://mlsanity.com/paper/2208.04494/) (2022)
- [Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks](https://mlsanity.com/paper/2212.12620/) (2022)
- [Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks](https://mlsanity.com/paper/2308.02194/) (2023)
- [Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network](https://mlsanity.com/paper/2407.01645/) (2024)
- [A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data](https://mlsanity.com/paper/2404.03493/) (2024)
- [Neuronal Competition Groups with Supervised STDP for Spike-Based Classification](https://mlsanity.com/paper/2410.17066/) (2024)
- [tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2206.08656/) (2022)
- [Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks](https://mlsanity.com/paper/2304.10749/) (2023)

## Abstract (arXiv)

Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their applicability in practical machine learning problems. The deep neural network-to-SNN conversion approach has been widely studied to broaden the applicability of SNNs. Most previous studies, however, have not fully utilized spatio-temporal aspects of SNNs, which has led to inefficiency in terms of number of spikes and inference latency. In this paper, we present T2FSNN, which introduces the concept of time-to-first-spike coding into deep SNNs using the kernel-based dynamic threshold and dendrite to overcome the aforementioned drawback. In addition, we propose gradient-based optimization and early firing methods to further increase the efficiency of the T2FSNN. According to our results, the proposed methods can reduce inference latency and number of spikes to 22% and less than 1%, compared to those of burst coding, which is the state-of-the-art result on the CIFAR-100.