A research breakthrough by Samsung Electronics has resulted in the world’s first in-memory computing based on MRAM (magnetoresistive random access memory). The research demonstrates Samsung’s advanced memory technology and its effort to merge memory and system semiconductors for next-generation artificial intelligence (AI) chips.
In the standard computer architecture, data is stored in memory chips and data computing is executed in separate processor chips. In contrast, in-memory computing is a new paradigm that seeks to perform both data storage and data computing in a memory network. Since this scheme can process a large amount of data stored within the memory network itself without having to move the data and also because the data processing in the memory network is executed in a highly parallel manner, power consumption is substantially reduced. In-memory computing has thus emerged as one of the promising technologies to realise next-generation low-power AI semiconductor chips.
For this reason, research on in-memory computing has been intensely pursued worldwide. Non-volatile memories, in particular RRAM (resistive random access memory) and PRAM (phase-change random access memory), have been actively used for demonstrating in-memory computing. By contrast, it has so far been difficult to use MRAM ─ another type of non-volatile memory ─ for in-memory computing despite merits such as operation speed, endurance and large-scale production. This difficulty stems from the low resistance of MRAM, due to which it cannot enjoy the power reduction advantage when used in the standard in-memory computing architecture.
The Samsung Electronics researchers have provided a solution to this issue through an architectural innovation resulting in an MRAM array chip that demonstrates in-memory computing by replacing the standard ‘current-sum’ in-memory computing architecture with a new ‘resistance sum’ alternative, which addresses the problem of small resistances of individual MRAM devices.
Samsung’s research team subsequently tested the performance of this MRAM in-memory computing chip by running it to perform AI computing. The chip achieved an accuracy of 98% in classification of hand-written digits and 93% accuracy in detecting faces from scenes.
The researchers have also suggested that not only can this new MRAM chip be used for in-memory computing, but it can also serve as a platform to download biological neuronal networks. This is along the line of the neuromorphic electronics vision that Samsung’s researchers recently put forward in a perspective paper published in the September 2021 issue of the journal Nature Electronics.
“In-memory computing draws similarity to the brain in the sense that in the brain, computing also occurs within the network of biological memories, or synapses; the points where neurons touch one another,” said Dr. Seungchul Jung, the first author of the paper. “In fact, while the computing performed by our MRAM network for now has a different purpose from the computing performed by the brain, such a solid-state memory network may in the future be used as a platform to mimic the brain by modelling the brain’s synapse connectivity.”
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