470 citations · 1.3k across the 24 of their papers we have counts for
8 papers · 1 filter
Sampling from exponential distributions in the time domain with superparamagnetic tunnel junctions
Temitayo N. Adeyeye, Sidra Gibeault, Daniel P. Lathrop +8
In the superparamagnetic regime, magnetic tunnel junctions switch between two resistance states due to random thermal fluctuations. The dwell time distribution in each state is exp…
Layer Ensemble Averaging for Improving Memristor-Based Artificial Neural Network Performance
Osama Yousuf, Brian Hoskins, Karthick Ramu +8
Artificial neural networks have advanced due to scaling dimensions, but conventional computing faces inefficiency due to the von Neumann bottleneck. In-memory computation architect…
Programmable electrical coupling between stochastic magnetic tunnel junctions
Sidra Gibeault, Temitayo N. Adeyeye, Liam A. Pocher +9
Superparamagnetic tunnel junctions (SMTJs) are promising sources of randomness for compact and energy efficient implementations of probabilistic computing techniques. Augmenting an…
Measurement-driven neural-network training for integrated magnetic tunnel junction arrays
William A. Borders, Advait Madhavan, Matthew W. Daniels +7
The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeti…
Device Modeling Bias in ReRAM-based Neural Network Simulations
Osama Yousuf, Imtiaz Hossen, Matthew W. Daniels +3
Data-driven modeling approaches such as jump tables are promising techniques to model populations of resistive random-access memory (ReRAM) or other emerging memory devices for har…
Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions
Jonathan M. Goodwill, Nitin Prasad, Brian D. Hoskins +9
The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Av…