New Cambridge human brain-inspired chip could slash AI energy use — new type of memristor has roughly a million times lower switching current than conventional devices

A new hafnium oxide memristor developed by researchers at the University of Cambridge operates at switching currents a million times lower than conventional devices, potentially reducing AI energy consumption significantly. The device, engineered by Dr. Babak Bakhit's team, can smoothly switch states at currents below 10 nanoamps, offering hundreds of distinct conductance levels. Memristors like these could lead to neuromorphic systems that cut computing power consumption by over 70%, eliminating the need for energy-intensive data transfers between memory and processing units. Despite promising advancements, a challenge lies in lowering the fabrication temperature to align with standard industry processes, as the current 700°C requirement exceeds CMOS manufacturing tolerances.