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 ...
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 ...
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 ...
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.
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 ...
An artificial intelligence created by the firm DeepMind has discovered a new way to multiply numbers, the first such advance in over 50 years. The find could boost some computation speeds by up to 20 ...
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 ...
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