Tech

New AI method slashes power use by two orders of magnitude

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 multiplication with a sparse, event-driven algorithm that activates only relevant neurons. The method cut energy consumption by 100 times on benchmark workloads while raising top-line accuracy by 1.8 percent. The technique was tested on transformer models up to 7 billion parameters using custom FPGA hardware.

Why it matters

Readers learn that efficiency gains can come from rethinking core arithmetic rather than scaling hardware. This shifts workflow from throwing more GPUs at problems to auditing which computations actually matter. Teams that adopt sparse activation patterns free up budget and carbon allowances for additional experiments.

Who's doing it

A team at MIT CSAIL led by Professor Vivienne Sze demonstrated the approach on a 1.3-billion-parameter language model. Their prototype ran on a single low-power FPGA board and matched or exceeded baseline accuracy while drawing under 5 watts during inference.

Try it

1. Download the open-source sparse inference library from https://github.com/mit-csaillab/sparse-transformer.
2. Convert a 1-billion-parameter model checkpoint into the sparse format using the provided conversion script.
3. Run inference on the FPGA board and compare watt-hour readings against a dense baseline to observe the 100x drop.

Read the original at sciencedaily.com