Neuromorphic computers modeled after the human brain can now solve the complex equations behind physics simulations — something once thought possible only with energy-hungry supercomputers. The ...

Scientists have taken a major step toward mimicking nature’s tiniest gateways by creating ultra-small pores that rival the dimensions of biological ion channels—just a few atoms wide. The breakthrough ...

Traditional computing systems struggle with dynamic adaptation and suffer from the separation of sensing, processing, and memory functions, leading to high energy consumption and latency. Neuromorphic ...

Spiking neural networks (SNNs) are an implementation of neuromorphic computing, an aspect of artificial intelligence and machine learning (AI/ML). Neuromorphic computing emulates the operation of ...

BUFFALO, N.Y. — It’s estimated it can take an AI model over 6,000 joules of energy to generate a single text response. By comparison, your brain needs just 20 joules every second to keep you alive and ...

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 ...

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 ...