# A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding

arXiv: https://arxiv.org/abs/2206.02495 · HTML: https://mlsanity.com/paper/2206.02495/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Efficient Spiking Neural Networks with Radix Encoding](https://mlsanity.com/paper/2105.06943/) (2021)
- [E3NE: An End-to-End Framework for Accelerating Spiking Neural Networks with Emerging Neural Encoding on FPGAs](https://mlsanity.com/paper/2111.10027/) (2021)

## Built on by (most cited first, 3 of 3)

- [DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs](https://mlsanity.com/paper/2305.05187/) (2023)
- [Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity](https://mlsanity.com/paper/2211.05412/) (2022)
- [Exploring the Sparsity-Quantization Interplay on a Novel Hybrid SNN Event-Driven Architecture](https://mlsanity.com/paper/2411.15409/) (2024)

## Abstract (arXiv)

Spiking neural networks (SNNs) recently gained momentum due to their low-power multiplication-free computing and the closer resemblance of biological processes in the nervous system of humans. However, SNNs require very long spike trains (up to 1000) to reach an accuracy similar to their artificial neural network (ANN) counterparts for large models, which offsets efficiency and inhibits its application to low-power systems for real-world use cases. To alleviate this problem, emerging neural encoding schemes are proposed to shorten the spike train while maintaining the high accuracy. However, current accelerators for SNN cannot well support the emerging encoding schemes. In this work, we present a novel hardware architecture that can efficiently support SNN with emerging neural encoding. Our implementation features energy and area efficient processing units with increased parallelism and reduced memory accesses. We verified the accelerator on FPGA and achieve 25% and 90% improvement over previous work in power consumption and latency, respectively. At the same time, high area efficiency allows us to scale for large neural network models. To the best of our knowledge, this is the first work to deploy the large neural network model VGG on physical FPGA-based neuromorphic hardware.