23 citations · 48 across the 10 of their papers we have counts for
10 papers
Magnetic Tunnel Junction Random Number Generators Applied to Dynamically Tuned Probability Trees Driven by Spin Orbit Torque
Andrew Maicke, Jared Arzate, Samuel Liu +7
Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNG) can consume orders of magnitude less energy per bit than CMOS pseudo-RNG. Here, we numerical…
Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems
Samuel Liu, Deji Akinwande, Dmitry Kireev +1
Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to digital systems.…
Stochastic Domain Wall-Magnetic Tunnel Junction Artificial Neurons for Noise-Resilient Spiking Neural Networks
Thomas Leonard, Samuel Liu, Harrison Jin +1
The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) make SNNs promising for edge applications that require high energy efficiency. To realize SNNs in h…
Parallel Matrix Multiplication Using Voltage Controlled Magnetic Anisotropy Domain Wall Logic
Nicholas Zogbi, Samuel Liu, Christopher H. Bennett +4
The domain wall-magnetic tunnel junction (DW-MTJ) is a versatile device that can simultaneously store data and perform computations. These three-terminal devices are promising for…
Probabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation
Suma G. Cardwell, Catherine D. Schuman, J. Darby Smith +7
Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficien…
Random Bitstream Generation using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions
Samuel Liu, Jaesuk Kwon, Paul W. Bessler +6
Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. The magnetic tunnel junction (MTJ) h…
Metaplastic and Energy-Efficient Biocompatible Graphene Artificial Synaptic Transistors for Enhanced Accuracy Neuromorphic Computing
Dmitry Kireev, Samuel Liu, Harrison Jin +4
CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is…
Shape-Dependent Multi-Weight Magnetic Artificial Synapses for Neuromorphic Computing
Thomas Leonard, Samuel Liu, Mahshid Alamdar +8
In neuromorphic computing, artificial synapses provide a multi-weight conductance state that is set based on inputs from neurons, analogous to the brain. Additional properties of t…
Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation
Samuel Liu, Christopher H. Bennett, Joseph S. Friedman +3
Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) devic…
Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence
Priyamvada Jadaun, Can Cui, Sam Liu +1
Neuromorphic computing mimics the organizational principles of the brain in its quest to replicate the brain's intellectual abilities. An impressive ability of the brain is its ada…