184 citations · 430 across the 19 of their papers we have counts for
21 papers
A self-heating electrochemical cell with nine decades of programmable linear resistance
Adam L. Gross, Sangheon Oh, Minseong Park +22
A programmable linear resistor with a compact footprint would have profound implications for microelectronics, enabling efficient in-sensor analog signal processing and in-memory c…
Analog fast Fourier transforms for scalable and efficient signal processing
T. Patrick Xiao, Ben Feinberg, David K. Richardson +7
Edge devices are being deployed at increasing volumes to sense and act on information from the physical world. The discrete Fourier transform (DFT) is often necessary to make this…
Nanoscale imaging of He-ion irradiation effects on amorphous TaO toward electroforming-free neuromorphic functions
Olha Popova, Steven J. Randolph, Sabine M. Neumayer +7
Resistive switching in thin films has been widely studied in a broad range of materials. Yet the mechanisms behind electroresistive switching have been persistently difficult to de…
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…
Tunable intervalence charge transfer in ruthenium Prussian blue analogue enables stable and efficient biocompatible artificial synapses
Donald A. Robinson, Michael E. Foster, Christopher H. Bennett +12
Emerging concepts for neuromorphic computing, bioelectronics, and brain-computer interfacing inspire new research avenues aimed at understanding the relationship between oxidation…
On the Accuracy of Analog Neural Network Inference Accelerators
T. Patrick Xiao, Ben Feinberg, Christopher H. Bennett +5
Specialized accelerators have recently garnered attention as a method to reduce the power consumption of neural network inference. A promising category of accelerators utilizes non…
High-Speed CMOS-Free Purely Spintronic Asynchronous Recurrent Neural Network
Pranav O. Mathews, Christian B. Duffee, Abel Thayil +8
Neuromorphic computing systems overcome the limitations of traditional von Neumann computing architectures. These computing systems can be further improved upon by using emerging 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…
Domain Wall Leaky Integrate-and-Fire Neurons with Shape-Based Configurable Activation Functions
Wesley H. Brigner, Naimul Hassan, Xuan Hu +7
Complementary metal oxide semiconductor (CMOS) devices display volatile characteristics, and are not well suited for analog applications such as neuromorphic computing. Spintronic…
Domain Wall-Magnetic Tunnel Junction Spin Orbit Torque Devices and Circuits for In-Memory Computing
Mahshid Alamdar, Thomas Leonard, Can Cui +8
There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing device…
Device-aware inference operations in SONOS nonvolatile memory arrays
Christopher H. Bennett, T. Patrick Xiao, Ryan Dellana +10
Non-volatile memory arrays can deploy pre-trained neural network models for edge inference. However, these systems are affected by device-level noise and retention issues. Here, we…
Unsupervised Competitive Hardware Learning Rule for Spintronic Clustering Architecture
Alvaro Velasquez, Christopher H. Bennett, Naimul Hassan +5
We propose a hardware learning rule for unsupervised clustering within a novel spintronic computing architecture. The proposed approach leverages the three-terminal structure of do…
Evaluating complexity and resilience trade-offs in emerging memory inference machines
Christopher H. Bennett, Ryan Dellana, T. Patrick Xiao +6
Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e.g. accuracy continues to scale with parameters and complexity in the face of po…
CMOS-Free Multilayer Perceptron Enabled by Four-Terminal MTJ Device
Wesley H. Brigner, Naimul Hassan, Xuan Hu +5
Neuromorphic computing promises revolutionary improvements over conventional systems for applications that process unstructured information. To fully realize this potential, neurom…
PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-efficient ReRAM
Aayush Ankit, Izzat El Hajj, Sai Rahul Chalamalasetti +7
The wide adoption of deep neural networks has been accompanied by ever-increasing energy and performance demands due to the expensive nature of training them. Numerous special-purp…
Maximized Lateral Inhibition in Paired Magnetic Domain Wall Racetracks for Neuromorphic Computing
C. Cui, O. G. Akinola, N. Hassan +4
Lateral inhibition is an important functionality in neuromorphic computing, modeled after the biological neuron behavior that a firing neuron deactivates its neighbors belonging to…
Shape-based Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron
Wesley H. Brigner, Naimul Hassan, Lucian Jiang-Wei +7
Spintronic devices based on domain wall (DW) motion through ferromagnetic nanowire tracks have received great interest as components of neuromorphic information processing systems.…
Using Floating Gate Memory to Train Ideal Accuracy Neural Networks
Sapan Agarwal, Diana Garland, John Niroula +7
Floating gate SONOS (Silicon-Oxygen-Nitrogen-Oxygen-Silicon) transistors can be used to train neural networks to ideal accuracies that match those of floating point digital weights…
Electroforming-Free TaOx Memristors using Focused Ion Beam Irradiations
J. L. Pacheco, D. L. Perry, D. R. Hughart +2
We demonstrate creation of electroforming-free TaOx memristive devices using focused ion beam irradiations to locally define conductive filaments in TaOx films. Electrical characte…
Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator
Matthew J. Marinella, Sapan Agarwal, Alexander Hsia +6
Neural networks are an increasingly attractive algorithm for natural language processing and pattern recognition. Deep networks with >50M parameters are made possible by modern GPU…
Degenerate Resistive Switching and Ultrahigh Density Storage in Resistive Memory
Andrew J. Lohn, Patrick R. Mickel, Conrad D. James +1
We show that, in tantalum oxide resistive memories, activation power provides a multi-level variable for information storage that can be set and read separately from the resistance…