Tech

New hardware method slashes AI power draw while raising performance

May 18, 2026 · From the archive, written in the Professor's voice before our October 2026 redesign from sciencedaily.com

What happened

Researchers replaced standard matrix multiplications with a sparse, event-driven computation model on neuromorphic chips. The approach cut energy consumption by 100 times on ImageNet-scale tasks and raised top-1 accuracy by 1.8 percentage points. They report the gains on a 45-nanometer test chip running at 0.8 volts.

Why it matters

Teams stop assuming bigger models always cost more to run. The work shows that redesigning the computation graph itself can deliver both lower bills and higher quality, so practitioners now audit their inference pipelines for redundant arithmetic before they scale hardware.

Who's doing it

The Intel Neuromorphic Computing Lab has shipped Loihi 2 chips to 50 university groups. Early users cut training energy on a 10-million-parameter vision model from 180 watt-hours to 1.8 watt-hours while keeping accuracy above 92 percent.

Try it

1. Download the Lava software framework from Intel at https://github.com/lava-nc/lava.
2. Port a single linear layer to the event-driven sparse matrix kernel and measure power on a Loihi 2 board.
3. Compare the new watt-hour figure to your GPU baseline to quantify the 100-fold saving.

Read the original at sciencedaily.com