99 citations · 274 across the 17 of their papers we have counts for
10 papers · 1 filter
CMOS-integrated superparamagnetic tunnel junction-based p-bit
Ju-Young Yoon, Nuno Cacoilo, Advait Madhavan +5
Probabilistic computers offer promising solutions for computationally hard problems in domains such as combinatorial optimization and machine learning. A key building block in thes…
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…
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…
Associative Memories Using Complex-Valued Hopfield Networks Based on Spin-Torque Oscillator Arrays
Nitin Prasad, Prashansa Mukim, Advait Madhavan +1
Simulations of complex-valued Hopfield networks based on spin-torque oscillators can recover phase-encoded images. Sequences of memristor-augmented inverters provide tunable delay…
Temporal State Machines: Using temporal memory to stitch time-based graph computations
Advait Madhavan, Matthew Daniels, Mark Stiles
Race logic, an arrival-time-coded logic family, has demonstrated energy and performance improvements for applications ranging from dynamic programming to machine learning. However,…
Storing and retrieving wavefronts with resistive temporal memory
Advait Madhavan, Mark D. Stiles
We extend the reach of temporal computing schemes by developing a memory for multi-channel temporal patterns or "wavefronts." This temporal memory re-purposes conventional one-tran…
Energy-efficient stochastic computing with superparamagnetic tunnel junctions
Matthew W. Daniels, Advait Madhavan, Philippe Talatchian +2
Superparamagnetic tunnel junctions (SMTJs) have emerged as a competitive, realistic nanotechnology to support novel forms of stochastic computation in CMOS-compatible platforms. On…