Datacenter Dynamics

Researchers claim they can cut AI training energy demands by 75%

Researchers at the University of Michigan say they can reduce the energy consumption of AI training by up to 75 percent. Deep learning models and large language models can be trained more efficiently ...
Semiconductor Engineering

Low-Power Heterogeneous Compute Cluster For TinyML DNN Inference And On-Chip Training

A new technical paper titled “DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training” was published by researchers at University of Bologna and ETH Zurich ...
Electronic Design

Lifelong-DNN Takes a Different Machine-Learning Path

Machine learning (ML) is a broad topic within the realm of artificial intelligence (AI). One of the more popular ML technologies is deep neural networks (DNNs), which have driven FPGA and GPGPU ...
TechNode

Briefing: Chinese researchers propose new, more efficient DNN training method

51001614 - robotic hand, accessing on laptop, the virtual world of information. concept of artificial intelligence and replacement of humans by machines. Every Wednesday and Friday, TechNode’s ...
Visual Studio Magazine

DNN Image Classification Using Keras

An example of an image classification problem is to identify a photograph of an animal as a "dog" or "cat" or "monkey." The two most common approaches for image classification are to use a standard ...