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.
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