As the long-predicted end of Moore's Law seems ever more imminent, researchers around the globe are seriously evaluating a profoundly different approach to large-scale computing inspired by biological principles. In the traditional von Neumann architecture, a powerful logic core (or several in parallel) operates sequentially on data fetched from memory. In contrast, "neuromorphic" computing distributes both computation and memory among an enormous number of relatively primitive "neurons," each communicating with hundreds or thousands of other neurons through "synapses." Ongoing projects are exploring this architecture at a vastly larger scale than ever before, rivaling mammalian nervous systems, and developing programming environments that take advantage of them. Still, the detailed implementation, such as the use of analog circuits, differs between the projects, and it may be several years before their relative merits can be assessed.
Researchers have long recognized the extraordinary energy stinginess of biological computing, most clearly in a visionary 1990 paper by the California Institute of Technology (Caltech)'s Carver Mead that established the term "neuromorphic." Yet industry's steady success in scaling traditional technology kept the pressure off.