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World’s first MRAM-based in-memory computing

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Figure 1: MRAM crossbar array. Credit: DOI: 10.1038 / s41586-021-04196-6

Samsung Electronics today announced the world’s first demonstration of in-memory computing based on MRAM (Magnetoresistive Random Access Memory). This innovation paper will be published online by Nature on January 12th (GMT) and will be published in the next print version of Nature. The paper, entitled “Crossbar Array of Magnetoresistive Memory Devices for In-Memory Computing,” integrates Samsung’s leadership in memory technology with memory and system semiconductors for the next generation of artificial intelligence (AI) chips. Introducing our efforts.

This research was led by the Samsung Advanced Institute of Technology (SAIT) in close collaboration with Samsung Electronics Foundry Business and Semiconductor R & D Center. Dr. Seungchul Jung, a staff researcher at SAIT, the lead author of the paper, Dr. Donhee Ham, a fellow at SAIT and a professor at Harvard University, and Dr. Sang Joon Kim, vice president of technology at SAIT. At the forefront of research.

In a standard computer architecture, data is stored on a memory chip and data computing is performed on a separate processor chip.

In-memory computing, in contrast, is a new computing paradigm that seeks to perform both data storage and data computing in memory networks. With this method, a large amount of data stored in the memory network can be processed without moving, and the data processing in the memory network is highly parallelized, which can significantly reduce power consumption. In this way, in-memory computing has emerged as one of the promising technologies for realizing the next generation of low-power AI semiconductor chips.

For this reason, research on in-memory computing has been enthusiastically pursued around the world. Non-volatile memory, especially RRAM (resistive random access memory) and PRAM (phase change random access memory), is being actively used to demonstrate in-memory computing. In contrast, so far it has been difficult to use another type of MRAM non-volatile memory for in-memory computing, despite the advantages of MRAM such as speed, durability, and mass production. .. This issue is due to the low resistance of the MRAM. This prevents MRAM from benefiting from power savings when used with standard in-memory computing architectures.

Researchers at Samsung Electronics have provided a solution to this problem through architectural innovations. Specifically, to develop an MRAM array chip that demonstrates in-memory computing by replacing the standard “total current” in-memory computing architecture with a new “total resistance” in-memory computing architecture that addresses the problem. Succeeded. Of the small resistance of individual MRAM devices.

Samsung’s research team then tested the performance of this MRAM in-memory computing chip by running it to perform AI computing. The chip achieved 98% accuracy in classifying handwritten digits and 93% accuracy in detecting faces from the scene.

By taking MRAM memory, which has already achieved commercial scale production embedded in system semiconductor manufacturing, into the realm of in-memory computing, this work expands the frontier of next-generation low-power AI chip technology.

Researchers have also suggested that this new MRAM chip can be used not only for in-memory computing, but also as a platform for downloading biological neuron networks. This is in line with the vision of neuromorphic electronics recently proposed by Samsung researchers in a prospective paper published in the September 2021 issue of the journal Nature Electronics.

“In-memory computing brings out similarities to the brain in the sense that computing also occurs in the brain, within the biological memory or synaptic network where neurons come into contact with each other,” the paper said. .. “In fact, the computing performed by our MRAM networks now has a different purpose than the computing performed by the brain, but such solid-state memory networks will synapse the brain in the future. Connectivity that may be used as a platform to mimic the brain by modeling. “

As highlighted in this work, Samsung will continue to expand its leadership in next-generation computing and AI semiconductors by building cutting-edge memory technology and integrating it with system semiconductor technology.

Researchers are developing 128Mb STT-MRAM, the fastest embedded memory write speed in the world. More info: Seungchul Jung et al, Crossbar Array of Magnetoresistive Memory Devices for In-Memory Computing, Nature (2022). DOI: 10.1038 / s41586-021-04196-6

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