Description Firmware‑Resident Coordinate Awareness for Future Storage Devices License: Creative Commons BY‑NC 4.0 Spatially‑Aware NVMe proposes a forward‑looking architectural direction in which storage hardware becomes an active participant in geometry‑native AI execution. Building on the software mechanisms introduced in SGS S1, Axis‑Aware Layouts S2, Frozen Onion S3, and NSI S4, this paper explores how coordinate translation, mapper selection, temporal reference frames, and tier routing could migrate from host software into NVMe firmware. Modern SSD controllers already maintain mapping tables, manage multi‑tier flash hierarchies, and execute complex control logic; this work argues that extending these capabilities with spatio‑temporal awareness is a natural evolutionary step. The proposal separates control and data planes: firmware would handle (r, θ, φ) → LBA translation, layout‑aware prefetching, mapper history resolution, and tier allocation, while the NVMe protocol and data path remain unchanged. Analytical projections suggest that firmware‑resident coordination could reduce per‑access control overhead by 10–50× and enable layout‑aware prefetching that transforms random 4KB reads into sequential multi‑MB transfers under ideal conditions. While no hardware implementation exists, and all performance claims are theoretical, the architecture aligns with emerging trends in computational storage, semantic‑aware devices, and GPU‑direct I/O. This work is part of the NSI Core Series, outlining how geometry‑native execution could evolve from software runtimes into future storage hardware. Abstract Spatially‑Aware NVMe outlines a hardware direction in which NVMe firmware becomes aware of Spherical Grid Storage (SGS) coordinates, axis‑aware layouts, and Frozen Onion temporal semantics. Instead of treating storage as a passive byte array, the device would execute coordinate translation, mapper selection, and tier routing internally, reducing host‑side overhead and enabling layout‑aware prefetching. The architecture separates control and data planes: firmware handles geometry‑aware logic, while the NVMe protocol remains unchanged for backward compatibility. Analytical projections suggest that firmware‑resident coordination could reduce control overhead from ~1–5 μs (host software) to ~100–500 ns (firmware), a 10–50× improvement. Layout‑aware prefetching could convert random access patterns into sequential transfers, with theoretical bandwidth utilization improvements up to 145× under ideal conditions. Realistic system‑level estimates suggest 10–20× end‑to‑end improvement, pending empirical validation. This is not computational storage in the traditional sense; it is control‑plane offload for geometry‑native execution. No hardware exists, and all performance claims are theoretical. The proposal is a conceptual roadmap for future storage architectures aligned with the NSI framework. Background This work is the fifth entry in the NSI Core Series, extending the software architecture toward potential hardware realization: Spherical Grid Storage (SGS) — 10.5281/zenodo.18665189 Axis‑Aware Layouts — 10.5281/zenodo.18665191 Frozen Onion: Temporal Reference Frames for Zero‑Copy Memory Systems — 10.5281/zenodo.18665193 Neuron Smart Inference (NSI) — 10.5281/zenodo.18665206 Spatially‑Aware NVMe Devices for AI Workloads — 10.5281/zenodo.18665227 Methodological foundations are documented in: Intuitive‑Theoretic Synthesis (ITS) — 10.5281/zenodo.17633100 The Practice of Human‑AI Synthesis — 10.5281/zenodo.17763521 The Minimal Knowledge Paradox — 10.5281/zenodo.17931472 Design as Epistemological Pathway — 10.5281/zenodo.18067554 Key Contributions Architectural proposal for firmware‑resident coordinate‑aware storage Control/data plane separation enabling backward‑compatible NVMe evolution Domain profile framework for declarative geometry configuration Layout‑aware prefetching mechanisms for structured AI workloads Analytical performance model with uncertainty quantification Hardware feasibility assessment based on modern NVMe controller capabilities Conceptual bridge between NSI software runtimes and future storage devices Research Impact This work contributes to storage architecture, hardware‑software co‑design, and AI infrastructure by: Proposing a path for geometry‑native execution to migrate into hardware Demonstrating how firmware‑resident coordination could reduce overhead Aligning with industry trends in computational storage and semantic‑aware devices Highlighting the role of storage controllers as active orchestration components Offering a conceptual roadmap for future NVMe evolution tailored to AI workloads Extending the NSI architecture beyond software into potential silicon realization Access and Documentation ORCID: https://orcid.org/0009-0003-4876-9273 Academia.edu: https://independent.academia.edu/MarceloTeixeira214 LinkedIn: https://www.linkedin.com/in/marcelo-emanuel-paradela-teixeira-702082382/ Email: marcelo.soul.ai@gmail.com License: CC BY-NC 4.0 © Marcelo Emanuel Paradela Teixeira 2026
Marcelo Emanuel Paradela Teixeira (Mon,) studied this question.