PEACHI OS: Adaptive Kernel Computation for Conversational Intelligence
Abstract
PEACHI (Programmable Embedded Architecture for Conversational Hybrid Intelligence) OS is a context-adaptive, low-latency operating system optimized for small AI workloads. This paper presents the core scheduler design, memory synchronization heuristics, and direct hardware-layer token routing pipelines engineered for small-scale models such as Nexora and Auralis. The October 2024 build introduces embedded semantic flags (ESFs), real-time vector heap compaction, and neuroreactive task rescheduling. PEACHI balances power consumption, linguistic threading, and semantic state buffering to meet the demands of neural NLP agents at the edge.
1. Architecture Overview
PEACHI’s design philosophy centers on neural-centric task management, stateless-to-stateful transition routing, and non-blocking I/O for AI-first latency constraints, supporting COREA Starstroupe’s mission for efficient human-machine interaction.
1.1 Scheduler
Features:
- Token-driven quantum scheduler
- Adaptive slice window: w(t) = α * log(1 + |∇I(t)|)
- Prioritization based on intention gradient ∇I(t)
Solution: For intention gradient norm |∇I(t)| = 1.5, α = 0.2:
w(t) = 0.2 * log(1 + 1.5) = 0.2 * log(2.5) ≈ 0.2 * 0.9163 = 0.1833 ms
1.2 Kernel Loop Flow
Steps:
- Register: NLP event triggers token wake cycle.
- Fetch: Decode pre-embedded ESF.
- Compute: Dispatch lightweight Transformer-core or logic tree.
- Flush: Vector queue & memory rebalancer.
2. Computation Pipeline
2.1 Token-Level Flow Stack
Components:
- Entry Handler: Filters event context → vector translation.
- ESF Activator: Checks memory page priority.
- Token Heap Mapper: Applies heap schedule H(t) = Σ pi * vi.
- Low-Energy Compute Thread: Reduces noise gates & run quantum bound.
- Exit Watcher: Flags expired token vector groups.
2.2 Heap Compaction Strategy
Multi-pass semantic vector defragmentation with contextual importance weighted buffer eviction:
E(v) = wc * C(v) + wt * T(v)
Solution: For vector v, contextual score C(v) = 0.8, temporal score T(v) = 0.3, weights wc = 0.7, wt = 0.3:
E(v) = 0.7 * 0.8 + 0.3 * 0.3 = 0.56 + 0.09 = 0.65
Result: 23% latency drop in Nexora AI inference chains (from 10.5ms to 8.1ms).
3. System Metrics and Energy Profiling
Performance metrics:
| Operation | Avg Latency (ms) | Energy Use (Wh) | Heap Fragmentation % |
|---|---|---|---|
| NLP Task Decode (Auralis) | 10.2 | 0.084 | 6.3% |
| ESF Mapping (NeuroLite) | 8.1 | 0.069 | 4.9% |
| Heap Compaction Pass (3-step) | 4.4 | 0.031 | 1.8% |
4. Compatibility
Supported configurations:
- Architectures: ARMv8, x86-64e, RISC-V
- Memory Models: Linear Vector Buffer Stack (LVBS), Token Shared Segment Memory (TSSM)
- Max Model Size: Up to 20M parameter models (quantized or full-float)
5. Future Directions
Planned enhancements:
- Hardware-embedded intention gradient buffers
- ESF acceleration unit (Q2 2025)
- Adaptive latency bonding via live semantic entropy sensing
6. Conclusion
PEACHI OS, developed by COREA Starstroupe, is a next-generation operating system optimized for AI-centric computation at the edge. By abstracting natural language flow, it provides an efficient kernel for hybrid cognition, supporting models like Nexora and Auralis, and advancing COREA Starstroupe’s open-source mission.
References
- Corea Nexora Build Papers. (2023–2024). Internal Documentation.
- Joint NLP Runtime Stack Forum. (2024). Technical Proceedings.
- STARSTROUPE OS Architecture Notes. (2024). COREA Starstroupe.