Multiplying two rectangular number arrays, known as matrix multiplication, plays a crucial role in today's AI models, including speech and image recognition, chatbots from every major vendor, AI image generators, and video synthesis models like Sora.
By contrast, the new research, conducted by Ran Duan and Renfei Zhou of Tsinghua University, Hongxun Wu of the University of California, Berkeley, and by Virginia Vassilevska Williams, Yinzhan Xu, and Zixuan Xu of the Massachusetts Institute of Technology, seeks theoretical enhancements by aiming to lower the complexity exponent, ω, for a broad efficiency gain across all sizes of matrices.
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