Tech

New Hardware Design Slashes AI Power Draw by 100 Times

May 17, 2026 · From the archive, written in the Professor's voice before our October 2026 redesign from sciencedaily.com

What happened

Researchers replaced dense matrix multiplications with sparse tensor operations on neuromorphic chips. The method cut energy consumption from 500 joules per inference to 5 joules while raising top 1 accuracy on ImageNet from 76 percent to 79 percent.

Why it matters

This shows that hardware aware algorithm design can outperform pure software scaling. Users should test sparse models on edge devices before defaulting to cloud GPUs for every task.

Who's doing it

The Neuromorphic Computing Lab at Intel achieved 50 times lower power on their Loihi 2 chip when running keyword spotting models for voice assistants.

Try it

1. Visit https://www.intel.com/content/www/us/en/research/neuromorphic-computing.html and download the Loihi 2 SDK.
2. Convert your dense PyTorch model to sparse format using the provided conversion script.
3. Run inference on the Loihi 2 board and measure milliwatts per inference to confirm the power drop.

Read the original at sciencedaily.com