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Abstract Anomaly detection in real-time using autoencoders implemented on edge devices is exceedingly challenging due to limited hardware, energy, and computational resources. We show that these limitations can be addressed by designing an autoencoder with low-resolution non-volatile memory-based synapses and employing an effective quantized neural network learning algorithm. We further propose nanoscale ferromagnetic racetracks with engineered notches hosting magnetic domain walls (DW) as exemplary non-volatile memory-based autoencoder synapses, where limited state (5-state) synaptic weights are manipulated by spin orbit torque (SOT) current pulses to write different magnetoresistance states. The performance of anomaly detection of the proposed autoencoder model is evaluated on the NSL-KDD dataset. Limited resolution and DW device stochasticity aware training of the autoencoder is performed, which yields comparable anomaly detection performance to the autoencoder having floating-point precision weights. While the limited number of quantized states and the inherent stochastic nature of DW synaptic weights in nanoscale devices are typically known to negatively impact the performance, our hardware-aware training algorithm is shown to leverage these imperfect device characteristics to generate an improvement in anomaly detection accuracy (90.98%) compared to accuracy obtained with floating-point synaptic weights that are extremely memory intensive. Furthermore, our DW-based approach demonstrates a remarkable reduction of at least three orders of magnitude in weight updates during training compared to the floating-point approach, implying significant reduction in operation energy for our method. This work could stimulate the development of extremely energy efficient non-volatile multi-state synapse-based processors that can perform real-time training and inference on the edge with unsupervised data. 

Physical Reservoir Computing (PRC) is an unconventional computing paradigm that exploits the nonlinear dynamics of reservoir blocks to perform temporal data classification and prediction tasks. Here, we show with simulations that patterned thin films hosting skyrmion can implement energy-efficient straintronic reservoir computing (RC) in the presence of room-temperature thermal perturbation. This RC block is based on strain-induced nonlinear breathing dynamics of skyrmions, which are coupled to each other through dipole and spin-wave interaction. The nonlinear and coupled magnetization dynamics were exploited to perform temporal data classification and prediction. Two performance metrics, namely Short-Term Memory (STM) and Parity Check (PC) capacity are studied and shown to be promising (4.39 and 4.62 respectively), in addition to showing it can classify sine and square waves with 100% accuracy. These demonstrate the potential of such skyrmion based PRC. Furthermore, our study shows that nonlinear magnetization dynamics and interaction through spin-wave and dipole coupling have a strong influence on STM and PC capacity, thus explaining the role of physical interaction in a dynamical system on its ability to perform RC.