What does this equation mean?

Two things move $\eta$ in practice, and both are visible in the published record rather than being vendor marketing claims. The first is architecture: Yu-Hsin Chen, Tushar Krishna, Joel Emer, and Vivienne Sze’s Eyeriss accelerator demonstrated that a “row-stationary” dataflow — one that keeps partial sums and weights resident in local memory rather than repeatedly re-fetching them from off-chip DRAM — could deliver roughly an order of magnitude better energy efficiency than a comparable mobile GPU on the same convolutional workload, purely from reducing data movement, without a smaller manufacturing process [ 5 ] . Data movement, not arithmetic, dominates the energy cost of running a neural network on real hardware, and that fact is the organizing idea behind essentially all edge accelerator design.