99 citations · 274 across the 17 of their papers we have counts for
19 papers
Stochastic binary networks with asymmetric and time-delayed interactions
Hantao Zhang, Sidra Gibeault, Matthew W. Daniels +4
Stochastic binary networks are widely used to describe collective dynamics in complex systems and to perform neuromorphic computation, yet realistic networks often contain both asy…
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…
Wafer-Level Prototyping Tools for CMOS Bioelectronic Sensors
Advait Madhavan, Ruohong Shi, Alokik Kanwal +5
Integrating biology with complementary metal-oxide-semiconductor (CMOS) sensors can enable highly parallel measurements with minimal parasitic effects, significantly enhancing sens…
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…
Measurement-driven Langevin modeling of superparamagnetic tunnel junctions
Liam A. Pocher, Temitayo N. Adeyeye, Sidra Gibeault +7
Superparamagnetic tunnel junctions are important devices for a range of emerging technologies, but most existing compact models capture only their mean switching rates. Capturing q…
Entropy-Assisted Nanosecond stochastic operation in perpendicular superparamagnetic tunnel junctions
Lucile Soumah, Louise Desplat, Nhat-Tan Phan +7
We demonstrate a good agreement between mean dwell times measured in 50~nm diameter, perpendicularly magnetized superparamagnetic tunnel junctions (SMTJ), and theoretical predictio…
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…
Unbiased Random Number Generation using Injection-Locked Spin-Torque Nano-Oscillators
Nhat-Tan Phan, Nitin Prasad, Abderrazak Hakam +9
Unbiased sources of true randomness are critical for the successful deployment of stochastic unconventional computing schemes and encryption applications in hardware. Leveraging na…
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…
Mutual control of stochastic switching for two electrically coupled superparamagnetic tunnel junctions
Philippe Talatchian, Matthew W. Daniels, Advait Madhavan +5
Superparamagnetic tunnel junctions (SMTJs) are promising sources for the randomness required by some compact and energy-efficient computing schemes. Coupling SMTJs gives rise to co…
Computing and Memory Technologies based on Magnetic Skyrmions
Hamed Vakili, Wei Zhou, Chung T Ma +13
Solitonic magnetic excitations such as domain walls and, specifically, skyrmionics enable the possibility of compact, high density, ultrafast,all-electronic, low-energy devices, wh…
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,…
Temporal Memory with Magnetic Racetracks
Hamed Vakili, Mohammad Nazmus Sakib, Samiran Ganguly +5
Race logic is a relative timing code that represents information in a wavefront of digital edges on a set of wires in order to accelerate dynamic programming and machine learning a…
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…
Streaming Batch Eigenupdates for Hardware Neuromorphic Networks
Brian D. Hoskins, Matthew W. Daniels, Siyuan Huang +5
Neuromorphic networks based on nanodevices, such as metal oxide memristors, phase change memories, and flash memory cells, have generated considerable interest for their increased…