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Implementations of artificial neural networks that borrow analogue techniques could potentially offer low-power alternatives to fully digital approaches1–3. One notable example is in-memory computing based on crossbar arrays of non-volatile memories4–7that execute, in an analogue manner, multiply–accumulate operations prevalent in artificial neural networks. Various non-volatile memories—including resistive memory8–13, phase-change memory14,15and flash memory16–19—have been used for such approaches. However, it remains challenging to develop a crossbar array of spin-transfer-torque magnetoresistive random-access memory (MRAM)20–22,  despite the technology’s practical advantages such as endurance and large-scale commercialization5. The difficulty stems from the low resistance of MRAM, which would result in large power consumption in a conventional crossbar array that uses current summation for analogue multiply–accumulate operations. Here we report a 64 × 64 crossbar array based on MRAM cells that overcomes the low-resistance issue with an architecture that uses resistance summation for analogue multiply–accumulate operations. The array is integrated with readout electronics in 28-nanometre complementary metal–oxide–semiconductor technology. Using this array, a two-layer perceptron is implemented to classify 10,000 Modified National Institute of Standards and Technology 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). We also use the array to implement a single layer in a ten-layer neural network to realize face detection with an accuracy of 93.4 per cent.

The absence of electromagnetic wave motivates various studies of new communication channels in near field regime. An approach studied here is based on resonant inductive coupling and the use of Resonator Isolation (RI) system. The main concept of the RI system is to physically isolate two inductively coupled resonators from the signal input/output components. The resonators are charged with initial energy, and communicate by exchanging this energy according to the fundamental phenomenon of electromagnetic theory. Analyzing the energy exchange phenomenon, this paper presents two corresponding communication channel models for the RI system. Based on these channel models, the system architecture of the RI system is implemented at a chip level which consists of three components: resonator, analog switch, and digital process. [ABSTRACT FROM PUBLISHER]