A New 1P1R Image Sensor with In-Memory Computing Properties Based on Silicon Nitride Devices

Vasileiadis, Nikolaos; Ntinas, Vasileios; Fyrigos, Iosif-Angelos; Karamani, Rafailia-Eleni; Ioannou‐Sougleridis, V.; Normand, P.; Karafyllidis, Ioannis; Sirakoulis, Georgios Ch.; Dimitrakis, Panagiotis

doi:10.1109/iscas51556.2021.9401586

Cited by 9 publications

(4 citation statements)

References 21 publications

(14 reference statements)

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“…Phase 5. Weight update: The conductance is updated on a row-by-row or column-by-column basis, as proposed in [28]. Evidence exists of the effectiveness of the two-pulse scheme as This hardware friendly training method combines the advantages of the energy efficiency of the 1S1M synapse array in performing the analogue MAC operation and the digital logic for realising the rest of the training process.…”

Section: B Hardware Friendly Training Methodsmentioning

confidence: 99%

EMSN: An Energy-Efficient Memristive Sequencer Network for Human Emotion Classification in Mental Health Monitoring

Dong

Han

et al. 2023

IEEE Trans. Consumer Electron.

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Mental health problems are an increasingly common social issue severely affecting health and well-being. Multimedia processing technologies via facial expression show appealing prospects in the consumer field for mental health monitoring, while still suffer from intensive computation and low energy efficiency. This paper proposes an energy-efficiency memristive sequencer network (EMSN) for human emotion classification, which offers an environmentally friendly approach for consumers with low cost and easily deployable hardware. Firstly, twodimensional (2D) materials are employed to construct an ecofriendly memristor, the efficacy and reliability of which are confirmed through performance testing. Then, a sequencer block is proposed using memristive circuits. Notably, it is a core component of the EMSN, consisting of a bidirectional long shortterm memory circuit, normalisation circuit module, and multilayer perception module. After combining some necessary function modules, the EMSN can be achieved. Furthermore, the proposed EMSN is applied for human emotion classification. The experimental results demonstrate that the proposed EMSN has advantages in computational efficiency and classification accuracy compared to existing mainstream methods, indicating an advancement in consumer health monitoring.

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Section: B Hardware Friendly Training Methodsmentioning

confidence: 99%

EMSN: An Energy-Efficient Memristive Sequencer Network for Human Emotion Classification in Mental Health Monitoring

Dong

Han

et al. 2023

IEEE Trans. Consumer Electron.

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“…The first steps have already been taken to combine memristive devices with photosensors. The described architecture of a 1D1R sensor for machine vision is a 20 × 20 or 32 × 32 matrix of SiN x memristive devices coupled to a photodiode or a phototransistor ( Vasileiadis et al, 2021a , b ). The coupling of memristors with photosensors shows that this approach can simulate some retinal functions ( Chen et al, 2018 ; Eshraghian et al, 2018 ).…”

Section: The Memristive Architecture Enables the Implementation Of Re...mentioning

confidence: 99%

“…Figure 8 illustrates the concept of analog memristive vision exploiting coupled memristors and photodiodes ( Vasileiadis et al, 2021a , b ). The 1D1R memristive sensor receives visual information ( Figure 8A ).…”

Section: The Memristive Architecture Enables the Implementation Of Re...mentioning

confidence: 99%

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Makarov

Lobov

Shchanikov

et al. 2022

Front. Comput. Neurosci.

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The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.

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“…Transition metal oxides (e.g., HfO x [27,28], TaO x [29,30], ZrO x [31,32], TiO x [33,34], and more complex compounds such as perovskites [35]), as well as SiO x , and GeO x , are considered promising insulator materials for memristors. Recently, intensive research has also been carried out on memristive structures based on SiN x [36,37]. This is of practical interest due to their compatibility with the standard technology for creating modern integrated circuits.…”

Section: Introductionmentioning

confidence: 99%

Silicon-Compatible Memristive Devices Tailored by Laser and Thermal Treatments

Koryazhkina

Филатов

Тихов

et al. 2022

JLPEA

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Nowadays, memristors are of considerable interest to researchers and engineers due to the promise they hold for the creation of power-efficient memristor-based information or computing systems. In particular, this refers to memristive devices based on the resistive switching phenomenon, which in most cases are fabricated in the form of metal–insulator–metal structures. At the same time, the demand for compatibility with the standard fabrication process of complementary metal–oxide semiconductors makes it relevant from a practical point of view to fabricate memristive devices directly on a silicon or SOI (silicon on insulator) substrate. Here we have investigated the electrical characteristics and resistive switching of SiOx- and SiNx-based memristors fabricated on SOI substrates and subjected to additional laser treatment and thermal treatment. The investigated memristors do not require electroforming and demonstrate a synaptic type of resistive switching. It is found that the parameters of resistive switching of SiOx- and SiNx-based memristors on SOI substrates are remarkably improved. In particular, the laser treatment gives rise to a significant increase in the hysteresis loop in I–V curves of SiNx-based memristors. Moreover, for SiOx-based memristors, the thermal treatment used after the laser treatment produces a notable decrease in the resistive switching voltage.

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A New 1P1R Image Sensor with In-Memory Computing Properties Based on Silicon Nitride Devices

Cited by 9 publications

References 21 publications

EMSN: An Energy-Efficient Memristive Sequencer Network for Human Emotion Classification in Mental Health Monitoring

EMSN: An Energy-Efficient Memristive Sequencer Network for Human Emotion Classification in Mental Health Monitoring

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Silicon-Compatible Memristive Devices Tailored by Laser and Thermal Treatments

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