Zenith Kernel
Zenith is an AI-native microkernel that reimagines operating system architecture for the age of intelligent computing.
Core Architecture
Probabilistic Scheduling
Traditional schedulers optimize for throughput or latency. Zenith introduces probabilistic scheduling:
Each task carries a confidence score indicating how critical its execution is for system goals.
AI-Watchdog
The AI-Watchdog uses a 1B parameter model to:
- Predict resource contention 100ms ahead
- Migrate threads preemptively to optimal cores
- Detect anomalies in system behavior
- Self-heal by restarting failed components
Zero-Latency Interrupts
| Feature | Traditional | Zenith |
|---|---|---|
| Context Switch | ~1000 cycles | ~100 cycles |
| Interrupt Latency | ~500ns | ~50ns |
| Memory Allocation | Variable | Predictable |
| Scheduling Decision | O(n) | O(1) |
Memory Management
Zenith's memory subsystem:
// Memory regions are typed
region<tensor> npu_memory; // Direct NPU access
region<neural> model_cache; // Compressed model storage
region<critical> kernel_space; // Guaranteed availability
// Allocation with QoS guarantees
let buffer = allocate(npu_memory, size=1GB, priority=Realtime);Integration with Aurelia
Aurelia programs compile to Zenith-native bytecode:
- Tensor operations → Direct NPU invocation
- Neural networks → Pre-allocated inference contexts
- System calls → Capability-based access control
Roadmap
- Alpha: Q3 2026 — Basic scheduling and AI-Watchdog
- Beta: Q1 2027 — Full memory management and device drivers
- Release: Q3 2027 — Production-ready with SkyOS integration
Zenith: Intelligence at the core.