Hardware fragmentation remains a persistent bottleneck for deep learning engineers seeking consistent performance.

A new technical paper titled “Energy-Aware Deep Learning on Resource-Constrained Hardware” was published by researchers at Imperial College London and University of Cambridge. “The use of deep ...

While the disruptive nature of AI is clearly seen across industries, there are still many challenges, including increasing workloads and inherent energy consumption. Given deep learning and big data ...

Researchers introduce the EAVM protocol, achieving 17 % lower energy use and 20 % longer network lifetime in IoT systems with ...