Low-Power Adiabatic/MTJ LIM-Based XNOR/XOR Synapse and Neuron for Binarized Neural Networks

Using binarized neural network (BNN) as an alternative to the conventional convolutional neural network is a promising candidate to answer the demand of using human brain-inspired in applications with limited hardware and power resources, such as biomedical devices, IoT edge sensors, and other battery-operated devices. Using nonvolatile memory elements like MTJ devices in a LiM-based architecture can eliminate the need to access and use external memory which can significantly reduce the power consumption and area overhead. In addition, by using adiabatic-based designs, a significant part of the consumed power can be recovered to the power source which leads to a huge reduction in power consumption which is vital in applications with limited power and hardware resources. In this paper by using nonvolatile MTJ devices in a LiM architecture and using adiabatic-based circuits, an XNOR/XOR synapse and neuron is proposed. The proposed design offers 97% improvement in comparison with its state-of-the-art counterparts in case of power consumption. Also, it achieves at least 7% lower area compared to other counterparts which makes the proposed design a promising candidate for hardware implementation of BNNs.