3k citations · 3.7k across the 11 of their papers we have counts for
12 papers
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…
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…
Memory-efficient training with streaming dimensionality reduction
Siyuan Huang, Brian D. Hoskins, Matthew W. Daniels +2
The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads. To minimize this overhead, especia…
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…
A scalable method to find the shortest path in a graph with circuits of memristors
Alice Mizrahi, Thomas Marsh, Brian Hoskins +1
Finding the shortest path in a graph has applications to a wide range of optimization problems. However, algorithmic methods scale with the size of the graph in terms of time and e…
In aqua electrochemistry probed by XPEEM: experimental setup, examples, and challenges
Slavomír Nemšák, Evgheni Strelcov, Hongxuan Guo +8
Recent developments in environmental and liquid cells equipped with electron transparent graphene windows have enabled traditional surface science spectromicroscopy tools, such as…
Stateful characterization of resistive switching TiO2 with electron beam induced currents
Brian D. Hoskins, Gina C. Adam, Evgheni Strelcov +4
Metal oxide resistive switches are increasingly important as possible artificial synapses in next generation neuromorphic networks. Nevertheless, there is still no codified set of…
Three-Dimensional Stateful Material Implication Logic
Gina C. Adam, Brian D. Hoskins, Mirko Prezioso +1
Monolithic three-dimensional integration of memory and logic circuits could dramatically improve performance and energy efficiency of computing systems. Some conventional and emerg…
Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors
M. Prezioso, F. Merrikh-Bayat, B. Hoskins +2
Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses - the key components of high-performance, analog neuromorphic network…
Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors
Mirko Prezioso, Farnood Merrikh-Bayat, Brian Hoskins +3
Despite all the progress of semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex makes the hardware implementation of neuromorphic netwo…
Phenomenological Modeling of Memristive Devices
Farnood Merrikh-Bayat, Brian Hoskins, Dmitri B. Strukov
We present a computationally inexpensive yet accurate phenomenological model of memristive behavior in titanium dioxide devices by fitting experimental data. By design, the model p…
High-Precision Tuning of State for Memristive Devices by Adaptable Variation-Tolerant Algorithm
Fabien Alibart, Ligang Gao, Brian Hoskins +1
Using memristive properties common for the titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relati…