Spin wave interference based efficient neuromorphic computing

We demonstrate the design of a neuromorphic hardware, spin wave interference device (SWID), utilizing micromagnetic simulations, for performing feature extraction and classification of binary digit patterns. The SWID aims to reduce the weight computations in artificial neural network (ANN) implementations allowing for low power computing and faster inference. We achieve the direct classification of multibit binary input pulse schemes through synaptic behaviour and interference of spin waves. We showcase the versatility of SWID's information processing capabilities across two-bit ranges, 4-bit to 6-bit binary digit data, by effectively controlling the non-linearity and interference of spin waves with external input current pulses. The performance of the SWID with 4-bit and 6-bit digit pattern classification ability are tested for image recognition task with the Modified National Institute of Standards and Technology (MNIST) handwritten image database in a feed forward neural network. Though achieving 84.7% accuracy in image recognition, this SWID-based network reduces the weight computation by 99.4% as compared to the software-ANN, showcasing its capability of faster decision making. This huge reduction in computations offers great benefit to ANN applications in edge devices and memory constraint devices. These results underscore the potential of spin wave based SWID in designing power efficient neuromorphic hardware.