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 ...
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 ...
Tech Xplore on MSN
Beyond electronics: Optical system performs feature extraction with unprecedented low latency
Many modern artificial intelligence (AI) applications, such as surgical robotics and real-time financial trading, depend on ...
Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Can artificial intelligence (AI) create its ...
AZoOptics on MSN
Researchers Build Ultra-Fast Optical Chip for Feature Extraction with Record-Low Latency
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 ...
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 ...
Researchers at DeepMind in London have shown that artificial intelligence (AI) can find shortcuts in a fundamental type of mathematical calculation, by turning the problem into a game and then ...
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