Categories: AI/ML News

Coordinated software tools could cut AI training energy use by up to 50%

Around half of the energy used to train AI can be saved without affecting speed or performance, using software developed at the University of Michigan Engineering. Now, U-M researchers are working with NVIDIA to integrate their new approach into Megatron-LM, NVIDIA’s open-source platform for training AI.

Programmable photonic chip uses light to accelerate AI training and cut energy use

Penn Engineers have developed the first programmable chip that can train nonlinear neural networks using light—a breakthrough that could dramatically speed up AI training, reduce energy use and even pave the way for fully light-powered computers.

April 16, 2025

In "AI/ML News"

Reduce energy consumption of your machine learning workloads by up to 90% with AWS purpose-built accelerators

June 21, 2023

In "FAANG"

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…

January 19, 2026

In "AI/ML News"

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