What does this equation mean?

The physical limitation keeping analog in-memory compute confined to inference on weights that rarely change is the flip side of its core trick: a conductance that can be read out to represent a weight can also drift over time, and writing a new one wears the device in a way reading does not, so retraining costs more, in time and device lifetime, than in a design where weights sit in ordinary digital memory. A simple accounting for why raw operations-per-watt numbers are not directly comparable across any of the four approaches in this piece is worth writing out once: $E_{\text{op}} = E_{\text{core}} + E_{\text{periphery}} + E_{\text{I/O}}$. where $E_{\text{core}}$ is the energy of the operation itself — the multiply-accumulate, the interference pattern, the qubit gate — and $E_{\text{periphery}}$ and $E_{\text{I/O}}$ are the data-converter, laser-and-detector, cryogenic, or spike-routing overhead surrounding it. A headline figure reporting only $E_{\text{core}}$ , which vendor materials frequently do, looks dramatically better than one reporting the whole sum, and each approach hides a different fraction of its true cost: analog crossbars in $E_{\text{periphery}}$ 's data converters, photonics in $E_{\text{I/O}}$ 's laser budget, quantum processors in $E_{\text{periphery}}$ 's dilution-refrigerator overhead. A single tera-operations-per-watt figure without knowing which terms it includes is close to meaningless, and comparing two such figures from two vendors compounds the problem.