Technology

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Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process.

The technique has not yet been peer-reviewed, but the researchers—Rui-Jie Zhu, Yu Zhang, Ethan Sifferman, Tyler Sheaves, Yiqiao Wang, Dustin Richmond, Peng Zhou, and Jason Eshraghian—claim that their work challenges the prevailing paradigm that matrix multiplication operations are indispensable for building high-performing language models.

They argue that their approach could make large language models more accessible, efficient, and sustainable, particularly for deployment on resource-constrained hardware like smartphones.

In the paper, the researchers mention BitNet (the so-called "1-bit" transformer technique that made the rounds as a preprint in October) as an important precursor to their work.

According to the authors, BitNet demonstrated the viability of using binary and ternary weights in language models, successfully scaling up to 3 billion parameters while maintaining competitive performance.

Limitations of BitNet served as a motivation for the current study, pushing them to develop a completely "MatMul-free" architecture that could maintain performance while eliminating matrix multiplications even in the attention mechanism.

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