# Exploring the Sparsity-Quantization Interplay on a Novel Hybrid SNN Event-Driven Architecture

arXiv: https://arxiv.org/abs/2411.15409 · HTML: https://mlsanity.com/paper/2411.15409/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?](https://mlsanity.com/paper/2202.03133/) (2022)
- [Efficient Hardware Acceleration of Sparsely Active Convolutional Spiking Neural Networks](https://mlsanity.com/paper/2203.12437/) (2022)
- [DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs](https://mlsanity.com/paper/2305.05187/) (2023)
- [A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding](https://mlsanity.com/paper/2206.02495/) (2022)
- [Design Space Exploration of Sparsity-Aware Application-Specific Spiking Neural Network Accelerators](https://mlsanity.com/paper/2310.16745/) (2023)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) offer potential advantages in energy efficiency but currently trail Artificial Neural Networks (ANNs) in versatility, largely due to challenges in efficient input encoding. Recent work shows that direct coding achieves superior accuracy with fewer timesteps than traditional rate coding. However, there is a lack of specialized hardware to fully exploit the potential of direct-coded SNNs, especially their mix of dense and sparse layers. This work proposes the first hybrid inference architecture for direct-coded SNNs. The proposed hardware architecture comprises a dense core to efficiently process the input layer and sparse cores optimized for event-driven spiking convolutions. Furthermore, for the first time, we investigate and quantify the quantization effect on sparsity. Our experiments on two variations of the VGG9 network and implemented on a Xilinx Virtex UltraScale+ FPGA (Field-Programmable Gate Array) reveal two novel findings. Firstly, quantization increases the network sparsity by up to 15.2% with minimal loss of accuracy. Combined with the inherent low power benefits, this leads to a 3.4x improvement in energy compared to the full-precision version. Secondly, direct coding outperforms rate coding, achieving a 10% improvement in accuracy and consuming 26.4x less energy per image. Overall, our accelerator achieves 51x higher throughput and consumes half the power compared to previous work. Our accelerator code is available at: https://github.com/githubofaliyev/SNN-DSE/tree/DATE25