Linked article summary: A team has built a prototype analog chip that performs matrix multiplication — the core operation in neural networks — using in-memory computing rather than shuttling data back and forth. In benchmarks it hit roughly 100x the energy efficiency of a comparable GPU on inference workloads, though at reduced numerical precision. Commercial availability is described as "years away."