Categories: AI/ML News

A memristor crossbar-based learning system for scalable and energy-efficient AI

Deep-learning models have proven to be highly valuable tools for making predictions and solving real-world tasks that involve the analysis of data. Despite their advantages, before they are deployed in real software and devices such as cell phones, these models require extensive training in physical data centers, which can be both time and energy consuming.

Computing scheme accelerates machine learning while improving energy efficiency of traditional data operations

Artificial intelligence (AI) models like ChatGPT run on algorithms and have great appetites for data, which they process through machine learning, but what about the limits of their data-processing abilities? Researchers led by Professor Sun Zhong from Peking University's School of Integrated Circuits and Institute for Artificial Intelligence set out…

September 27, 2024

In "AI/ML News"

MLOps and the evolution of data science

The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computer scientists and business leaders have taken note of the potential of the data. The information…

August 12, 2023

In "FAANG"

User-friendly system can help developers build more efficient simulations and AI models

The neural network artificial intelligence models used in applications like medical image processing and speech recognition perform operations on hugely complex data structures that require an enormous amount of computation to process. This is one reason deep-learning models consume so much energy.

February 4, 2025

In "AI/ML News"

AI Generated Robotic Content