Solving many scientific and technical applications entails the use of matrix multiplies somewhere in the algorithm and thus the computer code. With today’s multicore CPUs, proper use of complier ...

Many modern artificial intelligence (AI) applications, such as surgical robotics and real-time financial trading, depend on ...

Researchers have developed an optical computing system that performs feature extraction for quantitative trading with ...

A new optical feature extraction engine, dubbed OFE2, reaches 12.5 GHz, enhancing AI applications in healthcare and finance with unprecedented speed and efficiency.

The new version of AlphaZero discovered a faster way to do matrix multiplication, a core problem in computing that affects thousands of everyday computer tasks. DeepMind has used its board-game ...

Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually ...

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As hardware designers turn toward multicore processors to improve computing power, software programmers must find new programming strategies that harness the power of parallel computing. One technique ...

Researchers at Tsinghua University developed the Optical Feature Extraction Engine (OFE2), an optical engine that processes ...

Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the sparse matrix-vector multiplication (SpMV) is critical for ...