What does this equation mean?

The value of measuring $E_{\mathrm{inf}}$ specifically, rather than folding it into a generic active-power number, is that inference energy is what a model and compiler choice can actually move, while sleep and housekeeping power are largely a board-design and component-selection problem. This is also where standardized measurement earns its keep: before MLPerf Tiny, TinyML hardware and software claims were close to impossible to compare, because there was no shared methodology for what to measure or how, and the working group behind it built a suite that reports latency and energy alongside accuracy across four representative workloads — keyword spotting, visual wake words, small-image classification, and anomaly detection — specifically so that a “faster” or “lower-power” claim means the same thing across vendors [ 4 ] . Before that benchmark existed, an earlier survey of the field had already flagged the underlying problem directly: the TinyML community lacked any widely accepted, reproducible benchmark, which meant published numbers were essentially incomparable across papers and product datasheets alike [ 8 ] . A team budgeting power today should treat any competitor’s headline energy-per-inference figure the same way that finding recommends treating any unstandardized number — as a claim tied to a specific harness and workload, not a portable constant.