Measuring acoustic inference on edge hardware

An acoustic classifier is only one part of an embedded listening system. The microphone, sampling window, feature extraction and decision logic all contribute to its response time and energy use. Measuring the model alone leaves much of the device unaccounted for.

Define where latency starts

Specify whether the measurement starts at sound onset, at the end of an audio window or when model execution begins. A short inference time does not remove the time spent collecting the signal. Report these stages separately and include the delay before the device takes an action.

Budget the complete device

Separate peak inference power from average power over a representative operating cycle. Include sensing, processor wake-up, memory access and communications. Record the measurement point and duty cycle so another engineer can interpret the result.

Test the operating environment

Build evaluation recordings around the installation: microphone placement, machinery load, background noise and distance from the source. Keep recordings from the same session together when splitting datasets. Measure missed events and false triggers over time, not only accuracy on isolated clips.

Validate the compiled model

After quantization and compilation, compare device outputs with the reference pipeline on held-out recordings. Check unsupported operations, memory allocation and feature preprocessing. A successful export is not an accuracy test.

Publish enough to repeat the measurement

Record the board, firmware, model version, sample rate, window length and measurement setup. Report latency distributions and energy over a stated workload. This note describes a measurement method; it does not report a new Rochon hardware benchmark.

This engineering note describes an approach, not a new experimental result. Performance claims require a documented test setup and reproducible measurements.