$$E_{\text{op}} = E_{\text{core}} + E_{\text{periphery}} + E_{\text{I/O}}$$

Equation 4 · Future Hardware

Comparing the Main Approaches to Post-CMOS, Neuromorphic, Photonic, and Quantum AI Compute

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

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…

Meanings in this article

• $E_{\text{I/O}}$: the data-converter, laser-and-detector, cryogenic, or spike-routing overhead surrounding it.

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