Paper

ENF Technical Note 02

This technical note surveys the most relevant prior work and standards that shape the design space for Embedded Neural Firmware (ENF). It reviews the TinyML ecosystem and its prevailing assumptions (runtime interpreters, flexible toolchains, and operational updateability), then contrasts these with ENF's sealed, deterministic "compile-and-flash" model. The note summarizes secure-boot and device-identity approaches (including PUF-rooted trust), and examines energy-harvesting constraints that motivate voltage-threshold gating and burst-mode inference. It also outlines formal verification methods for bounded neural networks (e.g., SMT- based and abstract-interpretation approaches) and discusses why ENF-style architectural constraints can make verification more tractable. Finally, it highlights empirical results from adjacent domains that support ENF's core claims: firmware-class inference is feasible, diagnostics can be non-invasive, and strong guarantees require minimizing runtime variability. Overall, the purpose of this note is to position ENF precisely within existing literature-by identifying what exists, what is missing, and what ENF intentionally forbids.