# Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding

arXiv: https://arxiv.org/abs/2107.12374 · HTML: https://mlsanity.com/paper/2107.12374/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2021.

## Built on by (most cited first, 10 of 10)

- [HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise](https://mlsanity.com/paper/2110.11417/) (2021)
- [P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications](https://mlsanity.com/paper/2203.04737/) (2022)
- [Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?](https://mlsanity.com/paper/2112.12133/) (2021)
- [In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision](https://mlsanity.com/paper/2212.10881/) (2022)
- [Toward Efficient Hyperspectral Image Processing inside Camera Pixels](https://mlsanity.com/paper/2203.05696/) (2022)
- [Hoyer regularizer is all you need for ultra low-latency spiking neural networks](https://mlsanity.com/paper/2212.10170/) (2022)
- [Spiking Neural Networks with Dynamic Time Steps for Vision Transformers](https://mlsanity.com/paper/2311.16456/) (2023)
- [Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology](https://mlsanity.com/paper/2312.01213/) (2023)
- [SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams](https://mlsanity.com/paper/2505.19487/) (2025)
- [When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks](https://mlsanity.com/paper/2312.06900/) (2023)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) have emerged as an attractive alternative to traditional deep learning frameworks, since they provide higher computational efficiency in event driven neuromorphic hardware. However, the state-of-the-art (SOTA) SNNs suffer from high inference latency, resulting from inefficient input encoding and training techniques. The most widely used input coding schemes, such as Poisson based rate-coding, do not leverage the temporal learning capabilities of SNNs. This paper presents a training framework for low-latency energy-efficient SNNs that uses a hybrid encoding scheme at the input layer in which the analog pixel values of an image are directly applied during the first timestep and a novel variant of spike temporal coding is used during subsequent timesteps. In particular, neurons in every hidden layer are restricted to fire at most once per image which increases activation sparsity. To train these hybrid-encoded SNNs, we propose a variant of the gradient descent based spike timing dependent back propagation (STDB) mechanism using a novel cross entropy loss function based on both the output neurons' spike time and membrane potential. The resulting SNNs have reduced latency and high activation sparsity, yielding significant improvements in computational efficiency. In particular, we evaluate our proposed training scheme on image classification tasks from CIFAR-10 and CIFAR-100 datasets on several VGG architectures. We achieve top-1 accuracy of 66.46% with 5 timesteps on the CIFAR-100 dataset with ~125× less compute energy than an equivalent standard ANN. Additionally, our proposed SNN performs 5-300× faster inference compared to other state-of-the-art rate or temporally coded SNN models.