Christopher H. Bennett | DeepAI

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∙ 11/22/2021

Shape-Dependent Multi-Weight Magnetic Artificial Synapses for Neuromorphic Computing

In neuromorphic computing, artificial synapses provide a multi-weight co...

25 Thomas Leonard, et al. ∙

∙ 09/03/2021

On the Accuracy of Analog Neural Network Inference Accelerators

Specialized accelerators have recently garnered attention as a method to...

7 T. Patrick Xiao, et al. ∙

∙ 07/05/2021

High-Speed CMOS-Free Purely Spintronic Asynchronous Recurrent Neural Network

Neuromorphic computing systems overcome the limitations of traditional v...

6 Pranav O. Mathews, et al. ∙

∙ 01/08/2021

Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation

Neuromorphic computing with spintronic devices has been of interest due ...

0 Samuel Liu, et al. ∙

∙ 11/11/2020

Domain Wall Leaky Integrate-and-Fire Neurons with Shape-Based Configurable Activation Functions

Complementary metal oxide semiconductor (CMOS) devices display volatile ...

10 Wesley H. Brigner, et al. ∙

∙ 04/02/2020

Device-aware inference operations in SONOS nonvolatile memory arrays

Non-volatile memory arrays can deploy pre-trained neural network models ...

0 Christopher H. Bennett, et al. ∙

∙ 03/24/2020

Unsupervised Competitive Hardware Learning Rule for Spintronic Clustering Architecture

We propose a hardware learning rule for unsupervised clustering within a...

0 Alvaro Velasquez, et al. ∙

∙ 03/04/2020

Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning

Machine learning implements backpropagation via abundant training sample...

7 Christopher H. Bennett, et al. ∙

∙ 02/25/2020

Evaluating complexity and resilience trade-offs in emerging memory inference machines

Neuromorphic-style inference only works well if limited hardware resourc...

0 Christopher H. Bennett, et al. ∙

∙ 02/03/2020

CMOS-Free Multilayer Perceptron Enabled by Four-Terminal MTJ Device

Neuromorphic computing promises revolutionary improvements over conventi...

0 Wesley H. Brigner, et al. ∙

∙ 09/12/2017

Spatio-temporal Learning with Arrays of Analog Nanosynapses

Emerging nanodevices such as resistive memories are being considered for...

0 Christopher H. Bennett, et al. ∙

∙ 06/27/2016

Exploiting the Short-term to Long-term Plasticity Transition in Memristive Nanodevice Learning Architectures

Memristive nanodevices offer new frontiers for computing systems that un...

0 Christopher H. Bennett, et al. ∙

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