LiteRT focuses on efficient inference packaging, while LiteRT Micro targets environments without a full operating system and with fixed memory constraints. This separation helps teams align tool choices to device class.

Microcontroller deployment prioritizes deterministic behavior and compact binaries over general runtime flexibility. A narrow operator set is a feature in this context because it improves predictability.

Document conversion commands and runtime build options in scripts, not ad-hoc notes, so results remain reproducible across environments. Codifying this as a team standard improves repeatability.

Use a repeatable export and conversion path so model interfaces remain stable across versions. Version every conversion configuration to avoid silent mismatches between training and deployment artifacts.

Normalization, feature extraction, and tensor shape assumptions must be identical between offline evaluation and firmware execution. Even small parity gaps can dominate field errors.

Integration stalls when operator audits are deferred until late firmware stages. Validate compatibility as soon as architecture candidates are shortlisted.

Check model operator usage early against the target runtime profile and kernels. Late discovery of unsupported operations often forces costly architecture redesign near release.

If an operation is unsupported, choose between graph rewrite, custom kernels, or architecture adjustment based on risk and maintainability. Prioritize long-term support over short-term hacks.

Use on-target regression runs for representative inputs after each conversion change. Host inference parity alone is not sufficient for MCU readiness.

LiteRT Micro typically uses a pre-allocated tensor arena, so memory sizing must be validated under full pipeline load. Keep explicit headroom for firmware services and safety logic.

CMSIS-NN offers optimized neural network kernels for Arm Cortex-M processors and can improve inference efficiency when operator paths match supported kernels. Benchmark with representative workloads, not microbenchmarks alone.

Treat arena budget and kernel selection as release-governed parameters with explicit ownership. Unowned tuning variables become recurring incident sources.

Validate input integrity, tensor shapes, and deterministic outputs before performance tuning. This sequence isolates functional issues early and reduces wasted optimization effort.

Tune buffer reuse, scheduling cadence, and hot-path kernels only after functional correctness is stable. Correctness-first tuning prevents fast-but-wrong deployments.

Publish bring-up runbooks with expected memory peaks and known failure signatures for faster onboarding and support. Review it at each release checkpoint so assumptions remain current.

Frequent issues include shape mismatches, unsupported kernels, arena under-sizing, and inconsistent preprocessing between training and firmware. These failures are predictable when compatibility checks are skipped.

Use a staged debug sequence: interface parity, operator audit, memory profiling, then kernel tuning. A fixed sequence reduces trial-and-error and shortens integration timelines.

Verify conversion reproducibility, operator support, arena headroom, deterministic outputs, and sustained latency under firmware load. Include regression evidence from target hardware.

Promote only builds that pass both functional and operational gates with margin. This policy avoids unstable deployments that pass one dimension but fail in real usage.