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.
Introduction The process of deploying machine learning models is an important part of deploying AI technologies and systems to the real world. Unfortunately, the road to model deployment can be a tough one. The process of deployment is often characterized by challenges associated with taking a trained model — the…
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…
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…