The Hidden Cost of Edge AI
Edge AI lives or dies by data movement. In practice, the energy cost of moving bits often dwarfs the power used for computation, so the biggest gains come from keeping data local, compressing models, and exchanging only essential updates. Techniques such as Federated Averaging, top-k sparsification, and quantization illustrate the shift from raw throughput to communication efficiency. The future depends on architectures that fuse sensing, memory, and compute into one energy-aware, latency-conscious fabric.
