PEACHI OS: Foundational Implementation for Natural Language Processing Workloads

Ron Asnahon, March 2024

Abstract

This paper outlines the foundational implementation of PEACHI OS—a lightweight operating system designed from first principles for natural language processing workloads. As the underlying execution environment for future STARSTROUPE small models like Nexora and Auralis, PEACHI targets optimized runtime execution, reduced context-switching overhead, and token-based priority orchestration. In its March 2024 build, the OS emphasizes microkernel modularity, a deterministic memory map for vector operations, and preliminary scheduling for tokenized processes. While still early in scope, the OS demonstrates promising latency benchmarks and power efficiency on low-power platforms.

1. Introduction

Modern AI systems, particularly those for natural language processing, demand real-time token flow control over traditional user-driven I/O. PEACHI (Programmable Embedded Architecture for Conversational Hybrid Intelligence) OS is designed to prioritize tokens as the core unit of execution, enabling efficient runtime for small language models. This paper introduces the March 2024 framework, including preemptive microthread scheduling, memory-isolated execution pools, and a lexical-event interrupt system, aligning with COREA Starstroupe’s open-source mission.

2. Kernel Architecture

2.1 Microkernel Core

The kernel includes:

• Token Reactor Engine (TRE): Dispatches token-level signals.
• Vector Memory Layer (VML): Provides fixed-offset, zero-fragmentation buffers.
• Event Router (ER): Maps token classes to compute units.

2.2 Memory Mapping

Memory configuration:

• Pages: 4KB fixed
• Token Buffers: Aligned at 128B offsets
• Isolation Flag: Set if linguistic tag class exceeds priority threshold

Page efficiency is computed as:

P_eff = (V_total - V_void) / V_total

Solution: For V_total = 512KB, V_void = 33KB:

P_eff = (512 - 33) / 512 = 479 / 512 = 0.935 ≈ 93.5%

3. Scheduling Design

3.1 Token Thread Scheduler

Scheduler parameters:

• Quantum Bound: Fixed 6ms
• Token Queue Depth: Max 16
• Priority Computation:

Q_i = λ₁ * S_i + λ₂ * (1 / T_i)

Where:

• S_i: Semantic class weight
• T_i: Time since last dispatch
• λ₁ = 0.65, λ₂ = 0.35

Solution: For S_i = 0.8, T_i = 2ms:

Q_i = 0.65 * 0.8 + 0.35 * (1 / 2) = 0.52 + 0.175 = 0.695

3.2 Interrupt Routine (Early Phase)

Interrupt details:

• Event: Token Triggered
• Interrupt Vector: IV_0x23
• Routing time: ~3.4ms (on ARM Cortex-A72)

4. Early Performance Metrics

Benchmarked on Raspberry Pi 5 (8GB), kernel v0.3-alpha:

5. Computation Model

NLP processes are split into microthreads:

• Lexical Unit Decode (LUD)
• Context Flag Assignment (CFA)
• Vector Dispatch Engine (VDE)

For a 384-token sequence, total runtime is:

Total Runtime = Σ_i=1³⁸⁴ (t_decode,i + t_assign,i + t_dispatch,i)

Average values:

• t_decode = 0.0039ms
• t_assign = 0.0024ms
• t_dispatch = 0.0041ms

Solution:

t_total,i = 0.0039 + 0.0024 + 0.0041 = 0.0104ms

Total Runtime = 384 * 0.0104 = 3.9936ms ≈ 3.99ms

6. Known Limitations (March 2024 Build)

Current limitations:

• No asynchronous background compaction
• No embedded semantic signal interpreter (planned for October 2024)
• No GPU offload support
• Static token thresholds, non-learnable

7. Future Work (as of March 2024)

Planned developments:

• Semantic-driven heap scheduler
• Contextual token bursts (C-Turbo mode)
• Partial kernel offload to inference engine (e.g., Nexora runtime core)
• Lightweight ESF implementation (planned for October 2024)

References

• Corea STARSTROUPE Internal OS Design Notes. (2024). PEACHI v0.3-alpha.
• Corea Nexora Build Papers. (2023–2024). Internal Documentation.
• Joint NLP Runtime Stack Forum. (2024). Technical Proceedings.