Organic Non-Datacenter Intelligence: A Pure-Rust Full Quaternion Transformer for Edge Decision-Making via Irreversible Lossy Compression

Contemporary Artificial Intelligence paradigms prioritize scale over architectural affinity with physical hardware, resulting in unsustainable energy footprints and dependency on centralized GPU datacenters. We present ONIWA (Organic Non-datacenter Intelligence Without Abuse), a Pure-Rust Full Quaternion Transformer optimized for low-power edge platforms such as the Raspberry Pi 4. By mapping the four components of Hamilton quaternions (w, x, y, z) directly to 128-bit ARM NEON vector registers (float32x4_t), our non-commutative Hamilton product kernel executes in four SIMD cycles without vector padding overhead. Re-evaluating edge intelligence as irreversible lossy compression rather than autoregressive token generation, ONIWA condenses high-entropy representation states into a discrete decision space. On an unmodified ARM Cortex-A72 node, our zero-allocation forward pass achieves a deterministic inference latency of 40.27 ms (p50) within a flat resident memory footprint of 6.4 MB, slashing energy consumption by 85.0% compared to standard real-valued architectures. We demonstrate practical edge deployment as a Git pre-commit security gatekeeper with an append-only cryptographic provenance ledger, establishing a sustainable, private, and fully reproducible foundation for decentralized edge intelligence. Key Highlights: Zero Framework Overhead: 100% Pure Rust without Python, PyTorch, or CUDA dependencies. Hardware-Affine Algebra: Direct 1-to-1 mapping of Hamilton quaternions to 128-bit ARM NEON SIMD registers (float32x4_t). Edge Efficiency: Deterministic 40.27 ms inference, 6.4 MB resident memory (RSS), and 85.0% energy reduction on Raspberry Pi 4. Open Reproducibility: Tamper-proof cryptographic provenance ledger and full open source implementation. Codebase & Provenance Ledger:https://github.com/KazutoMakino/oniwa