Explore how neuromorphic chips and brain-inspired computing bring low-power, efficient intelligence to edge AI, robotics, and ...

Calculations show that injecting randomness into a quantum neural network could help it determine properties of quantum ...

Nvidia’s latest pitch for the future of graphics is not about more polygons or higher memory bandwidth, it is about teaching ...

Energy-efficient neural network computing represents a transformative approach to mitigating the increasing energy demands of modern artificial intelligence systems. By harnessing cutting-edge ...

Artificial intelligence is colliding with a hard physical limit: the energy and heat of conventional chips. As models scale into the trillions of parameters, simply throwing more silicon and ...

image: All-optical transcendental equation solver. (A) schematic diagram of the all-optical transcendental equation solver. (B) Top-view SEM image of the all-optical transcendental equation solver, ...

Research on ONNs began as early as the 1960s. To clearly illustrate the development history of ONNs, this review presents the evolution of related research work chronologically at the beginning of the ...

Despite the wild success of ChatGPT and other large language models, the artificial neural networks (ANNs) that underpin these systems might be on the wrong track. For one, ANNs are “super ...

The human brain is an astonishing organ, as any neuroscientist can attest. And its ability to collect, store, analyze and use information is intriguing to physicists, engineers and computer scientists ...

An RIT scientist has been tapped by the National Science Foundation to solve a fundamental problem that plagues artificial neural networks. Christopher Kanan, an assistant professor in the Chester F.