Categories: AI/ML News

New memristor training method slashes AI energy use by six orders of magnitude

In a Nature Communications study, researchers from China have developed an error-aware probabilistic update (EaPU) method that aligns memristor hardware’s noisy updates with neural network training, slashing energy use by nearly six orders of magnitude versus GPUs while boosting accuracy on vision tasks. The study validates EaPU on 180 nm memristor arrays and large-scale simulations.

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…

December 15, 2022

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Memristor chip breaks the capacity limit of brain-inspired associative memory

Researchers in the Department of Electrical and Computer Engineering of the Faculty of Engineering and the Centre for Advanced Semiconductors and Integrated Circuits (CASIC) at the University of Hong Kong (HKU) have made a breakthrough in brain-inspired computing. In collaboration with Hewlett Packard Labs, the team has developed a memristor…

September 9, 2026

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Unique memristor design with analog switching shows promise for high-efficiency neuromorphic computing

The growing use of artificial intelligence (AI)-based models is placing greater demands on the electronics industry, as many of these models require significant storage space and computational power. Engineers worldwide have thus been trying to develop neuromorphic computing systems that could help meet these demands, many of which are based…

November 8, 2024

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