Energy-Efficient Implementation of Spiking Recurrent Cells on FPGA

• URL: http://arxiv.org/abs/2605.10679v2
• Date: Tue, 12 May 2026 13:58:02 GMT
• Title: Energy-Efficient Implementation of Spiking Recurrent Cells on FPGA
• Abstract summary: Spiking Neural Networks (SNNs) can reduce energy consumption when spiking activity is sparse and the neuron model is hardware-friendly.<n>We present an FPGA accelerator for an SNN using Spiking Recurrent Cell (SRC) neurons, providing a trade-off between biological plausibility and hardware cost.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking Neural Networks (SNNs) can reduce energy consumption compared to conventional Artificial Neural Networks (ANNs) when spiking activity is sparse and the neuron model is hardware-friendly. However, biologically faithful models are often too costly to implement on FPGAs, whereas very simple models (e.g., IR/LIF) sacrifice part of the neuronal dynamics. In this work, we present an FPGA accelerator for an SNN using Spiking Recurrent Cell (SRC) neurons, providing a trade-off between biological plausibility and hardware cost. We propose a set of mathematical simplifications that remove costly unary operators (\textit{tanh}, \textit{exp}) and avoid floating-point arithmetic through scaling and piecewise-defined approximations. The complete network is implemented in VHDL and validated using spiking traces derived from the MNIST dataset. The weight matrices computed off-line are stored directly in LUT-registers without any adaptation. This demonstrates the robustness of SRC cells. Experiments were conducted on an Artix-7 XC7A200T clocked at 100 MHz. The reference implementation achieves 96.31\% accuracy with a 220-image spiking trace and a processing time of 1.7424 ms per digit. We then investigate accuracy/energy trade-offs by reducing the spiking trace length and quantizing synaptic weights down to 4 bits, achieving 93.32\% accuracy at 0.55 mJ per digit (55 images, 5-bit weights) and 92.89\% at 0.45 mJ (44 images, 4-bit weights). These results show that SRC-based SNNs can deliver competitive performance with reduced energy consumption, while preserving richer neuronal dynamics than standard LIF/IR models.

Related papers

• Reducing ANN-SNN Conversion Error via Residual Membrane Potential Alignment [52.910770415431244]
  Spiking Neural Networks (SNNs) serve as core architectures for neuromorphic computing.<n>ANN-SNN conversion circumvents issues by reusing well-trained ANN weights for low-latency, energy-efficient inference.<n>Existing conversion schemes suffer from severe accuracy drops at small timesteps, large inference delays and cumulative quantization errors.
  arXiv  Detail & Related papers  (2026-08-14T04:46:03Z)
• ReSCom: A Reconfigurable Spiking Neural Network Accelerator Using Stochastic Computing [0.0]
  ReSCom is a reconfigurable SNN accelerator that reduces hardware complexity while maintaining stable inference.<n>A unified reconfigurable design supports Integrate-and-Fire, Leaky-and-Fire, and Synaptic neuron models within a single hardware framework.
  arXiv  Detail & Related papers  (2026-06-11T16:44:08Z)
• Memristor-Based Spiking Neural Network Accelerator for Bio-inspired Interception Task [3.3128630520083977]
  Spiking neural networks (SNNs) provide event-driven and low-power computation inspired by biological neural systems.<n>This paper proposes an analog memristor-based spiking neural network (SNN) accelerator that integrates in-memory synaptic computation with analog integrate-and-fire (IF) neurons.
  arXiv  Detail & Related papers  (2026-05-29T13:34:07Z)
• Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamics [40.43988645674521]
  Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their complex neuronal dynamics and sparse binary activations (spikes) inspired by the biological neural system.<n>Traditional neuron models use iterative step-by-step dynamics, resulting in serial computation and slow training speed of SNNs.<n>Recently, parallelizable spiking neuron models have been proposed to fully utilize the massive parallel computing ability of graphics processing units to accelerate the training of SNNs.
  arXiv  Detail & Related papers  (2025-01-24T13:44:08Z)
• Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning [52.28945097811129]
  We propose an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences.<n>We reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear.<n>Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources.
  arXiv  Detail & Related papers  (2024-10-08T14:27:28Z)
• Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces [42.72938925647165]
  Intra-cortical brain-machine interfaces (iBMIs) have the potential to dramatically improve the lives of people with paraplegia. Current iBMIs suffer from scalability and mobility limitations due to bulky hardware and wiring. We are investigating hybrid spiking neural networks for embedded neural decoding in wireless iBMIs.
  arXiv  Detail & Related papers  (2024-09-06T17:48:44Z)
• SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection [10.702093960098104]
  Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven operations. We propose a novel training paradigm that first accumulates a large amount of redundant information through OR Residual Connection (ORRC) We then filters out the redundant information using the Synergistic Attention (SynA) module, which promotes feature extraction in the backbone while suppressing the influence of noise and useless features in the shortcuts.
  arXiv  Detail & Related papers  (2023-11-11T13:36:27Z)
• Masked Spiking Transformer [6.862877794199617]
  Spiking Neural Networks (SNNs) and Transformers have attracted significant attention due to their potential for high energy efficiency and high-performance nature. We propose to leverage the benefits of the ANN-to-SNN conversion method to combine SNNs and Transformers. We introduce a novel Masked Spiking Transformer framework that incorporates a Random Spike Masking (RSM) method to prune redundant spikes and reduce energy consumption without sacrificing performance.
  arXiv  Detail & Related papers  (2022-10-03T19:56:09Z)
• Spikformer: When Spiking Neural Network Meets Transformer [102.91330530210037]
  We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the self-attention mechanism. We propose a novel Spiking Self Attention (SSA) as well as a powerful framework, named Spiking Transformer (Spikformer)
  arXiv  Detail & Related papers  (2022-09-29T14:16:49Z)
• Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks [25.160063477248904]
  A convolutional neural network model is developed for detecting atrial fibrillation from electrocardiogram signals. The model demonstrates high performance despite being trained on limited, variable-length input data. The final model achieved a 91.1% model compression ratio while maintaining high model accuracy of 91.7% and less than 1% loss.
  arXiv  Detail & Related papers  (2022-06-14T11:47:04Z)
• A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding [6.047137174639418]
  Spiking neural networks (SNNs) recently gained momentum due to their low-power multiplication-free computing. SNNs require very long spike trains (up to 1000) to reach an accuracy similar to their artificial neural network (ANN) counterparts for large models. We present a novel hardware architecture that can efficiently support SNN with emerging neural encoding.
  arXiv  Detail & Related papers  (2022-06-06T10:56:25Z)
• Single-Shot Optical Neural Network [55.41644538483948]
  'Weight-stationary' analog optical and electronic hardware has been proposed to reduce the compute resources required by deep neural networks. We present a scalable, single-shot-per-layer weight-stationary optical processor.
  arXiv  Detail & Related papers  (2022-05-18T17:49:49Z)
• Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning [6.417011237981518]
  Spiking neural networks (SNNs) with leaky integrate and fire (LIF) neurons can be operated in an event-driven manner. We show that SNNs can be trained for sequential tasks and propose modifications to a network of LIF neurons. We then develop a training scheme to train the proposed SNNs with improved inherent recurrence dynamics.
  arXiv  Detail & Related papers  (2021-09-04T17:13:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.