🌊 LiquidSpikeFormer: Neuromorphic Hybrid SNN-Transformer Architecture

🚩 Overview

LiquidSpikeFormer is an advanced neuromorphic deep learning architecture specifically designed to tackle the complexities and challenges associated with processing sparse, asynchronous, and event-driven data from Dynamic Vision Sensors (DVS).

Leveraging the synergy of Spiking Neural Networks (SNNs), Liquid Neural Networks (LNNs), and state-of-the-art Transformer models, this project explores efficient and biologically plausible ways to significantly enhance accuracy, latency, and generalization performance in event-based recognition tasks.

---

🎯 Research Problem

Core Problem:

How can we effectively and efficiently leverage the rich temporal dynamics and sparsity inherent in event-based sensor data (e.g., DVS) to achieve high-accuracy, low-latency, and power-efficient recognition tasks?

Challenges Addressed:

• Temporal Sparsity: Handling asynchronous, irregularly spaced event data.
• Multiscale Dynamics: Integrating both fine-scale rapid and coarse-scale slower temporal patterns.
• Biological Plausibility: Designing neuromorphic-friendly, energy-efficient architectures.
• Generalization: Achieving robust performance despite limited labeled data.

---

📌 Current Model Architecture

The LiquidSpikeFormer combines multiple novel modules:

1️⃣ Spike Encoding at Multiple Temporal Scales:

• Dual (fine/coarse) spike encoders to capture rich multi-scale temporal information.

2️⃣ ConvSNN Spatiotemporal Blocks:

• Separate spatial and temporal convolutions for rich feature extraction from sparse spike-based events.

3️⃣ Liquid Time-Constant SNN (LNN-inspired blocks):

• Adaptive spiking dynamics through learnable liquid neural states, incorporating neuromodulation for context-dependent adaptation.

4️⃣ Transformer Sequence Modeling:

• Post-spiking transformers capturing long-term temporal dependencies and complex interactions within the spiking sequences.

5️⃣ Multiple Early-Exit Classifiers:

• Enables deep supervision, reduces inference latency, and improves model efficiency during training.

---

📈 Expected Impacts

Implementing this architecture is expected to:

• Significantly outperform traditional CNN/Transformer methods in event-based datasets.
• Achieve state-of-the-art accuracy and generalization on tasks like gesture recognition.
• Drastically reduce power consumption, making it ideal for real-time applications in IoT, robotics, autonomous systems, and neuromorphic hardware platforms.

---

🚀 Future Novelty & Research Directions

To push this research frontier further, we plan to explore:

• Dynamically Neuromodulated Liquid Transformers: Adaptive attention and positional embeddings modulated by global spike dynamics.
• Meta-Spiking Fusion with Hyper-SNNs: Leveraging spike-based hypernetworks for adaptive fusion strategies.
• Liquid Reservoir Transformers: Incorporating reservoir computing dynamics directly into transformer positional encoding.
• Spiking Equilibrium Attention Networks (SEANs): Integrating equilibrium network dynamics to dynamically determine computation depth per input sequence.
• Self-supervised Spiking Contrastive Predictive Coding (S-SCPC): Developing powerful spike-level representation learning methods for significantly enhanced generalization.

---

⚙️ Installation & Usage

Coming soon!

---

🧠 Contributing

We welcome contributions to explore these exciting research directions:

• Open issues for new research ideas or experiments.
• Submit pull requests for improving existing implementations.

---

📚 Publications

Coming soon!

---

🛠️ Contact

For collaboration or questions, please reach out to:

• Kavyansh Tyagi
• GitHub: KAVYANSHTYAGI

---

Let's push the boundaries of neuromorphic deep learning together! 🚀