Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing

@article{Fuller2019ParallelPO,
  title={Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing},
  author={Elliot J. Fuller and Scott Tom Keene and Armantas Melianas and Zhongrui Wang and Sapan Agarwal and Yiyang Li and Yaakov Tuchman and Conrad D. James and Matthew J. Marinella and J. Joshua Yang and Alberto Salleo and A. Alec Talin},
  journal={Science},
  year={2019},
  volume={364},
  pages={570 - 574}
}
Ionic floating-gate memories Digital implementations of artificial neural networks perform many tasks, such as image recognition and language processing, but are too energy intensive for many applications. Analog circuits that use large crossbar arrays of synaptic memory elements represent a low-power alternative, but most devices cannot update the synaptic weights uniformly or scale to large array sizes. Fuller et al. developed an integrated device, ionic floating-gate memory, that has the… Expand
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References

SHOWING 1-10 OF 27 REFERENCES
A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing.
TLDR
This work describes an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors, opening a path towards extreme interconnectivity comparable to the human brain. Expand
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
TLDR
The experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). Expand
Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices
TLDR
It is shown that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme, clarifying the pathway to neural algorithm accelerators capable of parallelism during both read and write operations. Expand
SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations
TLDR
This work demonstrates analog resistive switching devices that possess desired characteristics for neuromorphic computing networks with minimal performance variations using a single-crystalline SiGe layer epitaxially grown on Si as a switching medium. Expand
Energy Scaling Advantages of Resistive Memory Crossbar Based Computation and Its Application to Sparse Coding
TLDR
This paper presents a kernels-based architecture for sparse coding that can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Expand
All‐Solid‐State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing
Electronic synaptic devices are important building blocks for neuromorphic computational systems that can go beyond the constraints of von Neumann architecture. Although two-terminal memristiveExpand
Low-Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing.
TLDR
An electrochemical graphene synapse, where the electrical conductance of graphene is reversibly modulated by the concentration of Li ions between the layers of graphene, is presented and suggests that this simple, two-dimensional synapse is scalable in terms of switching energy and speed. Expand
Analogue signal and image processing with large memristor crossbars
TLDR
It is shown that reconfigurable memristor crossbars composed of hafnium oxide memristors on top of metal-oxide-semiconductor transistors are capable of analogue vector-matrix multiplication with array sizes of up to 128 × 64 cells. Expand
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
TLDR
This work monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer memristor neural network and achieves competitive classification accuracy on a standard machine learning dataset. Expand
Mechanisms for enhanced state retention and stability in redox-gated organic neuromorphic devices
Recent breakthroughs in artificial neural networks (ANNs) have spurred interest in efficient computational paradigms where the energy and time costs for training and inference are reduced. OneExpand
...
1
2
3
...