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Hardware Requirements

System Requirements

Minimum Requirements

  • RAM: 16 GB (32 GB recommended)
  • GPU: NVIDIA GPU with 8GB+ VRAM (RTX 3080/4080/H100)
  • Storage: 20 GB free space
  • CPU: Multi-core processor (Intel i7/AMD Ryzen 7+)

Recommended Requirements

  • RAM: 32 GB
  • GPU: NVIDIA GPU with 20GB+ VRAM (RTX 4090/H100)
  • Storage: 50 GB free space (SSD recommended)
  • CPU: High-end multi-core processor

Model-Specific Requirements

DistilGPT-2

  • Conversion Time: 2-5 minutes
  • VRAM: 4-8 GB
  • Model Size: ~500 MB

SmolLM2-1.7B-Instruct

  • Conversion Time: 1-3 hours
  • VRAM: 20 GB
  • Model Size: ~3.5 GB

Supported Hardware

Current Support

  • ✅ Software Simulation: Full support on CPU/GPU
  • ✅ NVIDIA GPUs: CUDA 11.8+ or 12.1+
  • ✅ PyTorch: >= 2.2, < 3.0 (matches requirements.txt)

Planned Support (Future Work)

  • ⏳ Intel Loihi-2: Neuromorphic hardware deployment (future work; no published hardware runs from this repo)
  • ⏳ BrainChip Akida: Edge neuromorphic processing
  • ⏳ SpiNNaker: Large-scale spiking neural network platform

Installation Notes

CUDA Installation

Install a torch build (>= 2.2, < 3.0) matching your CUDA runtime from the official index:

# For CUDA 11.8
pip install torch --index-url https://download.pytorch.org/whl/cu118

# For CUDA 12.1
pip install torch --index-url https://download.pytorch.org/whl/cu121

# For CPU only
pip install torch --index-url https://download.pytorch.org/whl/cpu

SpikingJelly Installation

# Latest pre-release version required
pip install spikingjelly[cuda] -U --pre

Performance Expectations

Conversion Performance

  • Simplified Mode: 2-15 minutes
  • Full Pipeline: 1-3 hours (with quantization and calibration)

Memory Usage

  • Peak VRAM: 20GB (SmolLM2-1.7B-Instruct)
  • System RAM: 16-32GB during conversion

Troubleshooting

Common Issues

  1. CUDA Out of Memory: Reduce batch size or use CPU fallback
  2. SpikingJelly Version: Ensure version >= 0.0.0.0.14
  3. PyTorch Compatibility: Use PyTorch >= 2.2, < 3.0

Performance Optimization

  1. Use SSD storage for faster I/O
  2. Close unnecessary applications during conversion
  3. Use simplified mode for initial testing