‘Sparse matrix’ simplifies use of machine learning

Article By : EE Times Asia

A newly developed data processing from NEC utilises computing and communications technologies to accelerate the performance of vector computers in machine learning.

Japanese computer giant NEC has developed data processing technology that leverages "sparse matrix" data structures to accelerate the execution of machine learning on vector computers by up to 50 times that of the Apache Spark big data framework.

In addition, the company said it has also developed middleware that incorporates sparse matrix structures in order to simplify the use of machine learning. As a result, users are able to easily launch this middleware from Python or Spark infrastructures, which are commonly used for data analysis, without special programming.

NEC said its next-generation vector computer is being developed to flexibly meet a wide range of price and performance needs. This data processing technology expands the capabilities of next-generation vector computers to include large-scale data analysis, such as machine learning, in addition to numerical calculation, the conventional specialty of vector computers.

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