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 ...

What if next-generation computing systems were able to adopt the human brain’s information processing capacity and energy efficiency? Researchers at Rochester Institute of Technology have begun to ...

Explore how neuromorphic chips and brain-inspired computing bring low-power, efficient intelligence to edge AI, robotics, and IoT through spiking neural networks and next-gen processors. Pixabay, ...

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 ...

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 ...

Dhireesha Kudithipudi (second from right), founding director of MATRIX at UTSA, chats with students during the NSF AI Spring School at UTSA's San Pedro I building. The research is part of a broader ...

As modern manufacturing increasingly relies on artificial intelligence (AI), automation, and real-time data processing, the need for faster and more energy-efficient computing systems has never been ...

Large scale datasets and information processing requirements, within complex environments, are continuously reaching unprecedented levels of sophistication, especially in the advent of artificial ...

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 ...

Although today’s computers can perform superhuman feats, even the best are no match for human brains at tasks like processing speech. But as Jessamyn Fairfield explains, a new generation of ...