Ultra-low power neuromorphic hardware show promise for energy-efficient AI computation
A team including researchers from Seoul National University College of Engineering has developed neuromorphic hardware capable of performing artificial intelligence (AI) computations with ultra-low power consumption. The research, published in the journal Nature Nanotechnology, addresses fundamental issues in existing intelligent semiconductor materials and devices while demonstrating potential for array-level technology.
Modern artificial intelligence systems rely on moving large amounts of data between memory and processors, a design that limits speed and increases energy use. The human brain works differently: it combines memory and computation within synapses, allowing fast, efficient learning and perception. Replicating this approach in hardware is a central…
A research team led by Dr. Yong-hun Kim and Dr. Jeong-Dae Kwon has successfully developed the world's first neuromorphic semiconductor device with high-density and high-reliability by developing a thin film of lithium-ion battery materials. They achieved this by producing ultra-thin lithium ions, a key material of lithium-ion batteries that have…
A team of researchers has developed a drone that flies autonomously using neuromorphic image processing and control based on the workings of animal brains. Animal brains use less data and energy compared to current deep neural networks running on GPUs (graphic chips). Neuromorphic processors are therefore very suitable for small…