Search Results for author:
Found 5 papers, 0 papers with code
Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection
While the limited number of quantized states and the inherent stochastic nature of DW synaptic weights in nanoscale devices are 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 trained weights.
Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting
By comparing our spintronic physical RC approach with energy load forecasting algorithms, such as LSTMs and RNNs, we conclude that the proposed framework presents good performance in achieving high predictions accuracy, while also requiring low memory and energy both of which are at a premium in hardware resource and power constrained edge applications.
Energy Efficient Learning with Low Resolution Stochastic Domain Wall Synapse Based Deep Neural Networks
We demonstrate that extremely low resolution quantized (nominally 5-state) synapses with large stochastic variations in Domain Wall (DW) position can be both energy efficient and achieve reasonably high testing accuracies compared to Deep Neural Networks (DNNs) of similar sizes using floating precision synaptic weights.
Passive frustrated nanomagnet reservoir computing
We therefore propose a reservoir that meets all of these criteria by leveraging the passive interactions of dipole-coupled, frustrated nanomagnets.
Modeling of Thermal Magnetic Fluctuations in Nanoparticle Enhanced Magnetic Resonance Detection
We present a systematic numerical modeling investigation of magnetization dynamics and thermal magnetic moment fluctuations of single magnetic domain nanoparticles in a configuration applicable to enhancing inductive magnetic resonance detection signal to noise ratio (SNR).
Mesoscale and Nanoscale Physics
