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Samsung unveiled world’s first computer with MRAM non-volatile magnetoresistive memory

According to the latest report, Samsung Electronics today officially announced the world’s first computer equipped with MRAM memory, related papers published on the “Nature” website, and will be published in the magazine soon.

Moreover, the title of the thesis is “Interleaved Arrays of Magnetoresistive Memory Devices for In-Memory Computing Computers”, which can be used for AI computing.

The technology was jointly developed by Samsung SAIT Research Institute and Samsung Electronics Foundry Business and Semiconductor R&D Center, and the first author of the paper is Dr. Seungchul Jung.

Most of the current computers use independent RAM memory and independent hard disk storage, but in order to improve computing efficiency, the industry has been developing non-volatile memory, which can take into account the functions of hard disk and memory, and help to greatly reduce power consumption.

To achieve this, researchers have developed RRAM (resistive random access memory), PRAM (phase-change random access memory), etc., and there are already prototypes. The MRAM (magnetoresistive non-volatile random access memory) used by Samsung has the advantages of high speed, durability, and easy mass production, but due to power consumption issues, it has not been officially unveiled before.

Samsung Electronics researchers provide solutions through innovative architectures. Specifically, the researchers successfully developed a new MRAM array that replaces the current architecture with a new “resistance” and computational architecture.

Moreover, the computer equipped with MRAM memory has passed the AI ​​computing performance test, the accuracy of recognizing handwritten numbers has reached 98%, and the accuracy of face recognition has reached 93%.

The researchers said that MRAM chips are used in in-memory computing (in-memory computing) computers, which are very suitable for neural network operations because this computing architecture is similar to the brain neuron network.

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