Thermal-aware Optimizations of ReRAM-based Neuromorphic Computing Systems

The paper discusses the main aspects of the practical implementation of software and user interfaces for managing models of neuromorphic computing systems.

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Control of the Boundary between the Gradual and Abrupt Modulation of Resistance in the Schottky Barrier Tunneling-Modulated Amorphous Indium-Gallium-Zinc-Oxide Memristors for Neuromorphic Computing

Electronics ◽

10.3390/electronics8101087 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1087 ◽

Cited By ~ 6

Author(s):

Jun Tae Jang ◽

Geumho Ahn ◽

Sung-Jin Choi ◽

Dong Myong Kim ◽

Dae Hwan Kim

Keyword(s):

Zinc Oxide ◽

Schottky Barrier ◽

Oxygen Content ◽

Bimodal Distribution ◽

Indium Gallium Zinc Oxide ◽

Metal Electrodes ◽

Neuromorphic Computing ◽

Computing Systems ◽

Proposed Model ◽

Indium Gallium

The transport and synaptic characteristics of the two-terminal Au/Ti/ amorphous Indium-Gallium-Zinc-Oxide (a-IGZO)/thin SiO2/p+-Si memristors based on the modulation of the Schottky barrier (SB) between the resistive switching (RS) oxide layer and the metal electrodes are investigated by modulating the oxygen content in the a-IGZO film with the emphasis on the mechanism that determines the boundary of the abrupt/gradual RS. It is found that a bimodal distribution of the effective SB height (ΦB) results from further reducing the top electrode voltage (VTE)-dependent Fermi-level (EF) followed by the generation of ionized oxygen vacancies (VO2+s). Based on the proposed model, the influences of the readout voltage, the oxygen content, the number of consecutive VTE sweeps on ΦB, and the memristor current are explained. In particular, the process of VO2+ generation followed by the ΦB lowering is gradual because increasing the VTE-dependent EF lowering followed by the VO2+ generation is self-limited by increasing the electron concentration-dependent EF heightening. Furthermore, we propose three operation regimes: the readout, the potentiation in gradual RS, and the abrupt RS. Our results prove that the Au/Ti/a-IGZO/SiO2/p+-Si memristors are promising for the monolithic integration of neuromorphic computing systems because the boundary between the gradual and abrupt RS can be controlled by modulating the SiO2 thickness and IGZO work function.

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Memristive Spiking Neural Networks Trained with Unsupervised STDP

Electronics ◽

10.3390/electronics7120396 ◽

2018 ◽

Vol 7 (12) ◽

pp. 396 ◽

Cited By ~ 8

Author(s):

Errui Zhou ◽

Liang Fang ◽

Binbin Yang

Keyword(s):

Neural Networks ◽

Feature Learning ◽

Spike Timing ◽

Spiking Neural Networks ◽

Learning Method ◽

Hardware Complexity ◽

Neuromorphic Computing ◽

Computing Systems ◽

Von Neumann ◽

Complex Architectures

Neuromorphic computing systems are promising alternatives in the fields of pattern recognition, image processing, etc. especially when conventional von Neumann architectures face several bottlenecks. Memristors play vital roles in neuromorphic computing systems and are usually used as synaptic devices. Memristive spiking neural networks (MSNNs) are considered to be more efficient and biologically plausible than other systems due to their spike-based working mechanism. In contrast to previous SNNs with complex architectures, we propose a hardware-friendly architecture and an unsupervised spike-timing dependent plasticity (STDP) learning method for MSNNs in this paper. The architecture, which is friendly to hardware implementation, includes an input layer, a feature learning layer and a voting circuit. To reduce hardware complexity, some constraints are enforced: the proposed architecture has no lateral inhibition and is purely feedforward; it uses the voting circuit as a classifier and does not use additional classifiers; all neurons can generate at most one spike and do not need to consider firing rates and refractory periods; all neurons have the same fixed threshold voltage for classification. The presented unsupervised STDP learning method is time-dependent and uses no homeostatic mechanism. The MNIST dataset is used to demonstrate our proposed architecture and learning method. Simulation results show that our proposed architecture with the learning method achieves a classification accuracy of 94.6%, which outperforms other unsupervised SNNs that use time-based encoding schemes.

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