Researchers develop fast, low-energy artificial synapse for advanced AI systems

Brain-inspired computing is a promising candidate for next-generation computing technologies. Developing next-generation advanced artificial intelligence (AI) systems that can be as energy-efficient, lightweight, and adaptable as the human brain has attracted significant interest. However, mimicking the brain's neuroplasticity, which is the ability to change a neural network connection, in traditional artificial synapses using ultralow energy is extremely challenging." An artificial synapse -- comprising a gap across two neurons to allow electrical signals to pass and communicate with each other -- can emulate the efficient neural signal transmission and memory formation process of the brain. To improve energy efficiency of the artificial synapse, Loke's research team has introduced a nanoscale deposit-only-metal-electrode fabrication process for artificial synapse for the first time. By using deposit-only nanopillar-based germanium-antimony-telluride memristive devices, the team designed a phase-change artificial synaptic device which has achieved an all-time-low energy consumption of 1.8 pJ per pair-pulse-based synaptic event. This is about 82% smaller compared to traditional artificial synapses. "The experiments have demonstrated that the artificial synapse based on phase-change materials could perform pair-pulse facilitation/depression, long-term potentiation/depression and spike timing dependent plasticity with ultralow energies.