High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning.

With Deep Reinforcement Learning, DeepMind has discovered an algorithm no human thought of. It is supposed to significantly accelerate matrix multiplication.

What do encrypted messages, recognizing speech commands and running simulations to predict the weather have in common? They all rely on matrix multiplication for accurate calculations. DeepMind, an ...

According to Google DeepMind, AlphaEvolve has successfully discovered multiple new algorithms for matrix multiplication, surpassing the previous AlphaTensor model in efficiency and performance (source ...

Since Volker Strassen proposed a recursive matrix multiplication algorithm reducing the time complexity to n2.81 in 1968, many scholars have done a lot of research on this basis. In recent years, ...

Can artificial intelligence (AI) create its own algorithms to speed up matrix multiplication, one of machine learning’s most fundamental tasks? Today, in a paper published in Nature, DeepMind ...

We present here the miss rate comparison of cache oblivious matrix multiplication using the sequential access recursive technique and normal multiplication program. Varying the cache size the ...