Explore how neuromorphic chips and brain-inspired computing bring low-power, efficient intelligence to edge AI, robotics, 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 ...
Los Alamos National Laboratory Researchers Design New Artificial Synapses for Neuromorphic Computing
Tested against a dataset of handwritten images from the Modified National Standards and Technology database, the interface-type memristors realized a high image recognition accuracy of 94.72%. (Los ...
This review describes various types of low-power memristors, demonstrating their potential for a wide range of applications. This review summarizes low-power memristors for multi-level storage, ...
A new technical paper titled “Neuromorphic Computing: A Theoretical Framework for Time, Space, and Energy Scaling” was published by researchers at Sandia National Laboratories. “Neuromorphic computing ...
Neuromorphic computing aims to replicate the functional architecture of the human brain by integrating electronic components that mimic synaptic and neuronal behaviours. Central to this endeavour are ...
A new technical paper titled “Survey of Deep Learning Accelerators for Edge and Emerging Computing” was published by researchers at University of Dayton and the Air Force Research Laboratory. “The ...
A computing approach that requires up to 8,000 times less energy than conventional methods is emerging as a potential answer to one of technology’s most significant challenges: the unsustainable ...
Our latest and most advanced technologies — from AI to Industrial IoT, advanced robotics, and self-driving cars — share serious problems: massive energy consumption, limited on-edge capabilities, ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback