Researchers at Samsung have developed a 64 x 64 crossbar arrays of memory to perform analog multiply-accumulate (MAC) operations using resistive memory, phase-change memory and flash memory.
MRAM is being adopted as an embedded non-volatile memory in leading-edge manufacturing processes for its endurance but its low resistance would result in large power consumption in a conventional crossbar array that uses current summation for analog MAC operations.
In-memory computing has emerged as one of the promising technologies to realize next-generation AI semiconductor chips because it allows local processing of data stored in memories and can minimize the movement of data thereby saving power consumption. The researchers have suggested that not only can this MRAM chip be used for in-memory computing, but it also can serve as a platform to download biological neuronal networks.
The array is integrated with readout electronics in 28nm CMOS. Using this array, a two-layer perceptron is implemented to classify 10,000 Modified National Institute of Standards and Technology (MNIST) digits with an accuracy of 93.23 per cent (software baseline: 95.24 per cent). In an emulation of a deeper, eight-layer visual geometry group-8 neural network with measured errors, the classification accuracy improves to 98.86 per cent (software baseline: 99.28 per cent). The array also implements a single layer in a ten-layer neural network to realize face detection with an accuracy of 93.4 per cent.
Next: Combined research
The research was led by Samsung Advanced Institute of Technology (SAIT) in close collaboration with Samsung Electronics Foundry Business and Semiconductor R&D Center.
“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 Seungchul Jung, the first-nmaed 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 solid-state memory network may in the future be used as a platform to mimic the brain by modelling the brain’s synapse connectivity.”
Related links and articles:
- Samsung presents AI processing-in-memory options
- Researchers want to ‘copy and paste’ the brain into memory
- Samsung embeds AI accelerator in memory chip
- Unsupervised in-memory AI learning goes digital
- Processor-in-memory DRAM benchmarked on Xeon server
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