Revolutionary AI breakthrough: Brain-inspired nanoelectronics slash energy consumption Researchers developed a brain-mimicking memristor using modified hafnium oxide, enabling ultra-low-power AI computation by integrating memory and processing like neurons. The new design reduces AI energy consumption by up to 70%, addressing the unsustainable power demands of current systems reliant on separate memory/processing units. Unlike filament-based memristors, the device uses p-n junctions for reliable, uniform switching at currents a million times lower, supporting analog in-memory computing. Fabrication requires high temperatures (~700°C), but efforts are underway to lower this for compatibility with standard semiconductor manufacturing. If scaled, this—alongside analog AI chips (e.g., Intel/Vidya’s sinusoidal activation MOSFETs)—could…