Spikepeft Parameter Efficient Snn Finetuning

---
name: spikepeft-parameter-efficient-snn-finetuning
title: SpikePEFT - Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models
version: 1.0.0
description: SpikePEFT framework for parameter-efficient adaptation of spiking neural networks on point cloud data. Uses Intrinsic Dynamics Tuning (IDT) and Silent-State Disambiguation Adaptation (SSDA) to achieve high accuracy while updating only ~5% of parameters.
tags:
  - spiking-neural-networks
  - parameter-efficient-fine-tuning
  - point-cloud-analysis
  - neuromorphic-computing
  - energy-efficient-ai
authors:
  - Zihao Guo
  - Jihua Zhu
  - Yiding Sun
  - Lin Chen
  - Danwei Wang
paper_url: https://arxiv.org/abs/2607.29048
arxiv_id: 2607.29048
published_date: 2026-07-31
---

# SpikePEFT: Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models

## Overview
SpikePEFT is the first parameter-efficient fine-tuning framework specifically designed for spiking point cloud models. It addresses two key challenges in SNN adaptation:
1. **Parameter overhead**: Full fine-tuning of pre-trained SNNs incurs substantial parameter and storage overhead
2. **Information loss**: Binary spike propagation suppresses task-relevant sub-threshold information

## Core Components

### Intrinsic Dynamics Tuning (IDT)
- **Purpose**: Enables efficient neuron-intrinsic adaptation while keeping pre-trained synaptic transformations frozen
- **Mechanism**: Adaptively modulates membrane decay and firing thresholds
- **Benefit**: Preserves energy efficiency while allowing task-specific adaptation

### Silent-State Disambiguation Adaptation (SSDA)
- **Purpose**: Recovers task-relevant information from informative silent states
- **Mechanism**: Provides richer evidence for downstream adaptation by leveraging sub-threshold dynamics
- **Benefit**: Enhances model performance without increasing spike activity

## Performance Results
- **ModelNet40**: 92.4% accuracy with only ~5% of trainable parameters updated
- **ScanObjectNN(PB_T50_RS)**: 85.6% accuracy on the most challenging classification split
- **Energy Efficiency**: Maintains the energy efficiency advantages of SNNs
- **Parameter Efficiency**: Updates only about 5% of trainable parameters compared to full fine-tuning

## Use Cases
Use SpikePEFT when:
- You need to adapt pre-trained spiking point cloud models to downstream tasks
- Resource constraints limit parameter storage and computation
- Energy efficiency is critical for deployment on neuromorphic hardware
- You want to preserve the event-driven computation benefits of SNNs

## Implementation Guidelines

### When to Apply
- Pre-trained SNN models exist for your point cloud domain
- Downstream task requires adaptation but full fine-tuning is too expensive
- Target hardware has limited memory or power budget

### Key Parameters to Tune
- Membrane decay modulation range for IDT
- Firing threshold adjustment bounds
- SSDA recovery strength for silent state disambiguation

### Evaluation Metrics
- Accuracy on target dataset
- Percentage of parameters updated
- Energy consumption during inference
- Synaptic operations (SynOps) count

## Integration with Existing Workflows
SpikePEFT can be integrated into existing SNN training pipelines by:
1. Loading pre-trained spiking point cloud models
2. Freezing synaptic weights and biases
3. Adding IDT modules to modulate neuron dynamics
4. Implementing SSDA layers to recover silent state information
5. Training only the IDT and SSDA parameters

## Future Directions
- Extend to other spiking modalities beyond point clouds
- Combine with other parameter-efficient methods like LoRA for SNNs
- Explore hardware-aware implementations for specific neuromorphic platforms

## References
- Guo, Z., Zhu, J., Sun, Y., Chen, L., & Wang, D. (2026). Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models. arXiv:2607.29048 [cs.CV].