# Exploring the Connection Between Binary and Spiking Neural Networks

arXiv: https://arxiv.org/abs/2002.10064 · HTML: https://mlsanity.com/paper/2002.10064/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 30 of 38)

- [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)
- [Neural Architecture Search for Spiking Neural Networks](https://mlsanity.com/paper/2201.10355/) (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)
- [One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency](https://mlsanity.com/paper/2110.05929/) (2021)
- [Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence](https://mlsanity.com/paper/2303.10276/) (2023)
- [TopSpark: A Timestep Optimization Methodology for Energy-Efficient Spiking Neural Networks on Autonomous Mobile Agents](https://mlsanity.com/paper/2303.01826/) (2023)
- [Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples](https://mlsanity.com/paper/2209.03358/) (2022)
- [Q-SNNs: Quantized Spiking Neural Networks](https://mlsanity.com/paper/2406.13672/) (2024)
- [Energy-efficient Knowledge Distillation for Spiking Neural Networks](https://mlsanity.com/paper/2106.07172/) (2021)
- [The fine line between dead neurons and sparsity in binarized spiking neural networks](https://mlsanity.com/paper/2201.11915/) (2022)
- [Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression](https://mlsanity.com/paper/2107.12445/) (2021)
- [SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks](https://mlsanity.com/paper/2206.09449/) (2022)
- [Adversarially Robust Spiking Neural Networks Through Conversion](https://mlsanity.com/paper/2311.09266/) (2023)
- [A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network from Convolutional Neural Network](https://mlsanity.com/paper/2103.00944/) (2021)
- [Single Channel Speech Enhancement Using U-Net Spiking Neural Networks](https://mlsanity.com/paper/2307.14464/) (2023)
- [Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive Gradient Modulation Mechanism](https://mlsanity.com/paper/2502.14344/) (2025)
- [Navigating Local Minima in Quantized Spiking Neural Networks](https://mlsanity.com/paper/2202.07221/) (2022)
- [Rethinking Pretraining as a Bridge from ANNs to SNNs](https://mlsanity.com/paper/2203.01158/) (2022)
- [Exploring Extreme Quantization in Spiking Language Models](https://mlsanity.com/paper/2405.02543/) (2024)
- [A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks](https://mlsanity.com/paper/2104.05124/) (2021)
- [HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2107.11979/) (2021)
- [Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks](https://mlsanity.com/paper/2304.10749/) (2023)
- [Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks](https://mlsanity.com/paper/2308.04672/) (2023)
- [Deep Unsupervised Learning Using Spike-Timing-Dependent Plasticity](https://mlsanity.com/paper/2307.04054/) (2023)
- [Benchmarking Spiking Neural Network Learning Methods with Varying Locality](https://mlsanity.com/paper/2402.01782/) (2024)
- [A survey on learning models of spiking neural membrane systems and spiking neural networks](https://mlsanity.com/paper/2403.18609/) (2024)
- [Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network](https://mlsanity.com/paper/2405.00699/) (2024)
- [BSO: Binary Spiking Online Optimization Algorithm](https://mlsanity.com/paper/2511.12502/) (2025)
- [S3NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks](https://mlsanity.com/paper/2201.10879/) (2022)

## Abstract (arXiv)

On-chip edge intelligence has necessitated the exploration of algorithmic techniques to reduce the compute requirements of current machine learning frameworks. This work aims to bridge the recent algorithmic progress in training Binary Neural Networks and Spiking Neural Networks - both of which are driven by the same motivation and yet synergies between the two have not been fully explored. We show that training Spiking Neural Networks in the extreme quantization regime results in near full precision accuracies on large-scale datasets like CIFAR-100 and ImageNet. An important implication of this work is that Binary Spiking Neural Networks can be enabled by "In-Memory" hardware accelerators catered for Binary Neural Networks without suffering any accuracy degradation due to binarization. We utilize standard training techniques for non-spiking networks to generate our spiking networks by conversion process and also perform an extensive empirical analysis and explore simple design-time and run-time optimization techniques for reducing inference latency of spiking networks (both for binary and full-precision models) by an order of magnitude over prior work.