Here is the uncomfortable arithmetic of edge intelligence. In Horowitz's canonical @45nm measurements (ISSCC 2014), an FP32 multiply costs about 3.7 pJ while an FP32 add costs about 0.9 pJ, so the multiply, one operation, is already ~4x the add. Stack them into a network where every MAC is one multiply plus one add and multiplies eat ~80% of the arithmetic budget. But arithmetic is the cheap part. Reading a single 32-bit word from on-chip SRAM costs ~5 pJ, more than any single op, and reaching out to DRAM costs ~640 pJ, that one fetch outweighs every add in this entire policy combined. This is the memory wall, the von-Neumann bottleneck stated in joules: on battery-powered hardware the energy is burned moving data and multiplying it, not 'thinking.' That reframes the whole game. If the multiply is the single most expensive arithmetic act and data movement dwarfs even that, then the highest-leverage move in physical AI is not a cleverer optimizer, it is to make the multiply disappear and to stop moving weights across the bus. Three states {-1,0,+1} do exactly that: multiply-by-{-1,0,+1} collapses to add / subtract / skip, and a log2(3)=1.585-bit weight barely has to move. You are about to see, in real numbers you compute yourself, why the frontier of edge AI is an energy-accounting frontier, and why ternary sits on the right side of it.