Neuromorphic engineering is an interdisciplinary field that combines principles from neuroscience, computer science, and electrical engineering to design artificial neural systems, often referred to ...

Scientists believe they may have discovered a way to rework the hardware of AI. By mimicking the synapses of the human brain. Technology is edging closer and closer to the super-speed world of ...

Efforts to build brain-inspired computer hardware have been underway for decades, but the field has yet to have its breakout moment. Now, leading researchers say the time is ripe to start building the ...

Cory Merkel, assistant professor of computer engineering at Rochester Institute of Technology, will represent the university as one of five collegiate partners in the new Center of Neuromorphic ...

Rochester Institute of Technology recently became one of the inaugural academic partners in the BrainChip University AI Accelerator Program. As part of the university-corporate partnership, RIT’s ...

As computer vision (CV) systems become increasingly power and memory intensive, they become unsuitable for high-speed and resource deficit edge applications - such as hypersonic missile tracking and ...

Neuromorphic computers, inspired by the architecture of the human brain, are proving surprisingly adept at solving complex ...

US researchers solve partial differential equations with neuromorphic hardware, taking us closer to world's first ...

Neuromorphic computing, inspired by the neural architectures and functions of biological brains, is revolutionizing the development of highly efficient, adaptive computing systems. In robotics, this ...

Brain-inspired computing promises cheaper, faster, more energy efficient processing, according to experts at a Beijing conference, who discussed everything from reverse engineering insect brains to ...

Tiny molecules that can think, remember, and learn may be the missing link between electronics and the brain. For more than ...

Neuromorphic computing, inspired by the brain, integrates memory and processing to drastically reduce power consumption compared to traditional CPUs and GPUs, making AI at the network edge more ...