# Event-Driven Neural Networks ## Technical Foundation
- Event-driven processing paradigms
- Sparse activation patterns
- Localized memory storage and computation
- Analog signal processing where advantageous ## Performance Characteristics
- Computational efficiency improvements of 90%+ compared to dense matrix operations
- Latency reductions to the sub-15µs range for critical inference tasks
- Zero data exposure to external networks during processing
- Deterministic timing suitable for real-time control applications ## Integration Points
1. **Hardware Level**: Specialized neuromorphic cores
3. **Software Level**: Through the CFFI bridge for seamless software-hardware co-design
4. **Application Level**: Enabling new classes of edge AI applications ## Future Directions
- Further reducing energy consumption through novel materials
- Increasing computational density via 3D integration
- Enhancing plasticity mechanisms for lifelong learning
- Expanding ecosystem support for developers ## Conclusion