Memristor-based synapse design and a case study in reconfigurable systems

Feng, Ji; Li, Hai; Wysocki, Bryant; Thiem, Clare; McDonald, Nathan

doi:10.1109/ijcnn.2013.6706776

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…All of these mentioned properties make it a potential candidate for implementing ANNs. So far, the existing research mainly focuses on the hardware design of the memristor-based synapse circuit and the corresponding multilayer neural network, as well as the realisation of the hardware-friendly training methodology [21][22][23][24][25][26][27][28][29]. For example, Kim proposed a memristor bridge synaptic circuit in [21,22].…”

Section: Introductionmentioning

confidence: 99%

Hybrid dual‐complementary metal–oxide–semiconductor/memristor synapse‐based neural network with its applications in image super‐resolution

Dong

Lai

et al. 2019

IET Circuits, Devices & Systems

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Biology-inspired neural computing is a potential candidate for the implementation of next-generation intelligent systems. Memristor is a passive electrical element with resistance-switching dynamics. Owing to its natural advantages of nonvolatility, nanoscale geometries, and variable conductance, memristor can effectively simulate the synaptic connecting strength between the neurones in the multilayer neural networks. This study presents a kind of memristor synapse-based multilayer neural network hardware architecture with a suitable training methodology. Specifically, a novel dual-complementary metaloxide-semiconductor/memristor synaptic circuit is presented, which is capable of performing the negative, zero, and positive synaptic weights via controlling the direction of current passing through the memristors. Then, the neurone circuit synthesised with multiple synaptic circuits and an activation unit is further designed, which can be utilised to constitute a compact multilayer neural network with fully connected configuration. Also, a hardware-friendly chip-in-the-loop training method is provided during the network training phase. For the verification purpose, the presented neural network is applied for the realisation of single image super-resolution reconstruction.

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Section: Introductionmentioning

confidence: 99%

Hybrid dual‐complementary metal–oxide–semiconductor/memristor synapse‐based neural network with its applications in image super‐resolution

Dong

Lai

et al. 2019

IET Circuits, Devices & Systems

View full text Add to dashboard Cite

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“…The human brain contains an average of hundred trillion synapses [32]. In an effort to develop the synaptic circuit, the memristor is being used to store synaptic weight value within a memristor as its memristance value [33][34][35][36][37][38][39][40][41][42]. However, in memristor synaptic weights, nonvolatility, linearity and multilevel are still unsolved problems since there is no enjoyable report in the field yet proposed, which addresses these three demanding properties concurrently [32].…”

Section: Introductionmentioning

confidence: 99%

“…For the memristor-based artificial neural network to learn effectively, the symmetry between the rise and fall of device memductance (memristor conductance) is crucial. So far, to perform zero, negative and positive synaptic weight computation, different kinds of memristor-based synaptic circuits are implemented [34][35][36][37][38][39]. Since these three synaptic weight value computation implementations are not possible using a single memristor as a synapse, a composite of memristors is being used to make a bipolarity weight [47].…”

Section: Introductionmentioning

confidence: 99%

Stochastic and novel generic scalable window function-based deterministic memristor SPICE model comparison and implementation for synaptic circuit design

2019

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A memristor is a nonlinear polarity-dependent fundamental circuit element. Due to these intrinsic properties of the device, analyzing a circuit that contains multiple memristors becomes complex. In this paper, we study the characteristics of multiple-memristor series (anti-series) and parallel (anti-parallel) connections, including their transient and stable state composite properties. Also, the existing phenomenological and physics-based memristor mathematical modeling techniques have been discussed for use in SPICE simulation environment. For making a standardized comparison between memristor stochastic and deterministic models, all models presented in this paper have been implemented in a single SPICE program. In addition to the well-known previously reported Joglekar and Biolek window functions, a modified Biolek window function and a novel generic scalable window function have been used to model the intrinsic nonlinearity of memristors effectively. Furthermore, electronic synapse circuits based on memristive devices in series and parallel connections and synaptic circuits based on CMOS transistor-memristor architecture have been presented and analyzed. Based on the obtained results, artificial synaptic circuit design limitation using a single memristor has been demonstrated.

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Memristor-based synapse design and a case study in reconfigurable systems

Cited by 2 publications

References 20 publications

Hybrid dual‐complementary metal–oxide–semiconductor/memristor synapse‐based neural network with its applications in image super‐resolution

Hybrid dual‐complementary metal–oxide–semiconductor/memristor synapse‐based neural network with its applications in image super‐resolution

Stochastic and novel generic scalable window function-based deterministic memristor SPICE model comparison and implementation for synaptic circuit design

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