Recent advances in neural network architectures have not only elevated the performance of artificial intelligence systems but have also prompted a transformative re‐evaluation of energy efficiency in ...

Listen to the first notes of an old, beloved song. Can you name that tune? If you can, congratulations -- it's a triumph of your associative memory, in which one piece of information (the first few ...

The representation of individual memories in a recurrent neural network can be efficiently differentiated using chaotic recurrent dynamics.

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at the ...

PPA constraints need to be paired with real workloads, but they also need to be flexible to account for future changes.

Johns Hopkins electrical and computer engineers are pioneering a new approach to creating neural network chips—neuromorphic accelerators that could power energy-efficient, real-time machine ...

Generative artificial intelligence (AI) — such as ChatGPT and Dalle-2 — is undoubtedly one of the most groundbreaking and discussed technologies in recent history. Its applications and related issues ...

Machine learning (ML), a subset of artificial intelligence (AI), has become integral to our lives. It allows us to learn and reason from data using techniques such as deep neural network algorithms.

A new technical paper titled “Hardware Acceleration for Neural Networks: A Comprehensive Survey” was published by researchers at Arizona State University. Abstract “Neural networks have become a ...

Researchers have published a programmable framework that overcomes a key computational bottleneck of optics-based artificial intelligence systems. In a series of image classification experiments, they ...