Chips With Neural Tissue Aim to Make AI More Energy Efficient

As generative AI systems advance, so too does their appetite for energy. Training and running large language models consumes vast amounts of electricity. AI’s energy demand is projected to double in the next five years, gobbling up 3 percent of total global electricity consumption. But what if AI chips could function more like the human brain, processing complex tasks with minimal energy? A growing chorus of scientists and engineers believes that the key might lie in organoid intelligence. AI enthusiasts were introduced to the concept of brain-inspired chips in July at the United Nations’ AI for Good Summit in Geneva. There, David Gracias, a professor of chemical and biomolecular engineering at Johns Hopkins University, gave a talk discussing the latest research he’s led on biochips and their applications to AI. Focused on nanotech, intelligent systems, and bioengineering, Gracias’s research team is among the first to build a functioning biochip that combines neural organoids with adva