175 citations · 648 across the 29 of their papers we have counts for
9 papers · 1 filter
Neuromorphic Hebbian learning with magnetic tunnel junction synapses
Peng Zhou, Alexander J. Edwards, Frederick B. Mancoff +3
Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency. Conventional approach…
Synchronous Unsupervised STDP Learning with Stochastic STT-MRAM Switching
Peng Zhou, Julie A. Smith, Laura Deremo +2
The use of analog resistance states for storing weights in neuromorphic systems is impeded by fabrication imprecision and device stochasticity that limit the precision of synapse w…
Experimental Demonstration of Neuromorphic Network with STT MTJ Synapses
Peng Zhou, Alexander J. Edwards, Fred B. Mancoff +3
We present the first experimental demonstration of a neuromorphic network with magnetic tunnel junction (MTJ) synapses, which performs image recognition via vector-matrix multiplic…
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…
Passive frustrated nanomagnet reservoir computing
Alexander J. Edwards, Dhritiman Bhattacharya, Peng Zhou +10
Reservoir computing (RC) has received recent interest because reservoir weights do not need to be trained, enabling extremely low-resource consumption implementations, which could…
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…
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…
Reservoir Computing with Planar Nanomagnet Arrays
Peng Zhou, Nathan R. McDonald, Alexander J. Edwards +3
Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained…
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…