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In this work, we propose valley-coupled spin-hall memories (VSH-MRAMs) based on monolayer WSe2. The key features of the proposed memories are (a) the ability to switch magnets with perpendicular magnetic anisotropy (PMA) via VSH effect and…
The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing "power wall" and "memory wall" problems. To resolve those problems, processing-in-memory (PIM)…
Binaural reproduction is rapidly becoming a topic of great interest in the research community, especially with the surge of new and popular devices, such as virtual reality headsets, smart glasses, and head-tracked headphones. In order to…
The Perpendicular Shape Anisotropy Spin Transfer Torque Magnetic Random Access Memory (PSA-STT-MRAM) is a recent concept proposed to maintain the thermal stability of standard MRAM at small diameters, considering thick vertical pillars as…
Spin-transfer torque magnetoresistive random access memory (STT-MRAM) is an attractive alternative to current random access memory technologies due to its non-volatility, fast operation and high endurance. STT-MRAM does though have…
Large language model (LLM) training is often bottlenecked by memory constraints and stochastic gradient noise in extremely high-dimensional parameter spaces. Motivated by empirical evidence that many LLM gradient matrices are effectively…
To incorporate prior knowledge as well as measurement uncertainties in the traditional long short term memory (LSTM) neural networks, an efficient sparse Bayesian training algorithm is introduced to the network architecture. The proposed…
Recent progresses in magnetoionics offer exciting potentials to leverage its non-linearity, short-term memory, and energy-efficiency to uniquely advance the field of physical reservoir computing. In this work, we experimentally demonstrate…
This paper presents physical modeling and benchmarking for two-terminal spin-orbit torque magnetic random-access memory (2T-SOT-MRAM). The results indicate that the common SOT materials that provide only in-plane torque can provide little…
Neural networks have demonstrated their outstanding performance in a wide range of tasks. Specifically recurrent architectures based on long-short term memory (LSTM) cells have manifested excellent capability to model time dependencies in…
In this paper, we propose an efficient predefined structured sparsity-based ex-situ training framework for a hybrid CMOS-memristive neuromorphic hardware for deep neural network to significantly lower the power consumption and computational…
Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even super-human…
The ever-increasing demand to extract temporal correlations across sequential data and perform context-based learning in this era of big data has led to the development of long short-term memory (LSTM) networks. Furthermore, there is an…
One essential feature in MRAM cells is the spin torque efficiency, which describes the ratio of the critical switching current to the energy barrier. Within this paper it is reported that the spin torque efficiency can be improved by a…
As artificial intelligence (AI) advances into diverse applications, ensuring reliability of AI models is increasingly critical. Conventional neural networks offer strong predictive capabilities but produce deterministic outputs without…
Recurrent Neural Networks (RNN) are widely used for learning sequences in applications such as EEG classification. Complex RNNs could be hardly deployed on wearable devices due to their computation and memory-intensive processing patterns.…
Perpendicular shape anisotropy (PSA) offers a practical solution to downscale spin-transfer torque Magnetic Random-Access Memory (STT-MRAM) beyond the sub-20 nm technology node whilst retaining thermal stability of the storage layer…
Future applications of spin-orbit torque will require new mechanisms to improve the efficiency for switching nanoscale magnetic tunnel junctions (MTJs), while also controlling the magnetic dynamics to achieve fast, nanosecond scale…
Memristors are non-volatile nano-resistors. Their resistance can be tuned by applied currents or voltages and set to a large number of levels between two limit values. Thanks to these properties, memristors are ideal building blocks for a…
The methods of Nuclear Magnetic Resonance belong to the best developed and often used tools for studying random motion of particles in different systems, including soft biological tissues. In the long-time limit the current mathematical…