Memristive and CMOS Devices for Neuromorphic Computing

Milo, Valerio; Malavena, Gerardo; Compagnoni, Christian Monzio; Ielmini, Daniele

doi:10.3390/ma13010166

Cited by 103 publications

(70 citation statements)

References 189 publications

(276 reference statements)

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“…The retina-inspired DS system in this work can respond to the object movement in real time and in situ. Energy consumption for each spike of the device operation is estimated to be 87.5 nJ (see Experimental Section), which is higher than previously reported memristive synapses, [40] mainly due to the relatively long retention time in the range 0.01-1 s. Reducing the retention time by engineering the device structure and materials can, thus, contribute to reduce the energy consumption. The DS circuit can be easily integrated within complementary metal-oxide-semiconductor (CMOS) image sensors to enable preprocessing of visual information in situ within the camera at no additional area cost, thus enabling the development of a biometric eye.…”

Section: Discussionmentioning

confidence: 85%

Neuromorphic Motion Detection and Orientation Selectivity by Volatile Resistive Switching Memories

Wang

Covi

Milozzi

et al. 2020

Advanced Intelligent Systems

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Motion detection is a primary visual function, crucial for the survival of animals in nature. Direction‐selective (DS) neurons can be found in multiple locations in the visual neural system, both in the retina and in the visual cortex. For instance, the DS ganglion cell in the retina provides a real‐time response to moving objects, which is much faster than the image recognition executed in the visual cortex. Such in‐retina biological signal processing capability is enabled by the spatiotemporal correlation within different receptive fields of the DS ganglion cells. Taking inspiration from the biological DS ganglion cells in the retina, the motion detection is demonstrated in an artificial neural network made of volatile resistive switching devices with short‐term memory effects. The motion detection arises from the spatiotemporal correlation between the adjacent excitatory and inhibitory receptive fields with short‐term memory synapses, closely resembling the physiological response of DS ganglion cells in the retina. The work supports real‐time neuromorphic processing of sensor data by exploiting the unique physics of innovative memory devices.

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Section: Discussionmentioning

confidence: 85%

Neuromorphic Motion Detection and Orientation Selectivity by Volatile Resistive Switching Memories

Wang

Covi

Milozzi

et al. 2020

Advanced Intelligent Systems

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“…EqSpike could also be sped up by building on dedicated hardware in analog or digital CMOS ( Schemmel et al, 2010 ; Qiao et al, 2015 ; Thakur et al, 2018 ; Frenkel et al, 2019 ; Park et al., 2020 ). Emerging nanotechnologies such as memristive synapses and nanoscale spiking oscillators are compelling candidates to scale up neuromorphic hardware due to their small size, their speed, and their low energy consumption ( Marković et al, 2020 ; Milo et al, 2020 ; Sebastian et al, 2020 ; Wang et al, 2020 ; Xi et al, 2020 ). These technologies are typically prone to imperfections such as the device-to-device variability, cycle-to-cycle variability, or the non-linearity in the conductance-to-voltage response, which are known to considerably jeopardize learning in memristive neural networks ( Ishii et al, 2019 ; Zhang et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

EqSpike: Spike-driven equilibrium propagation for neuromorphic implementations

et al. 2021

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Summary Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. Equilibrium propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based networks. In this work, we develop a spiking neural network algorithm called EqSpike, compatible with neuromorphic systems, which learns by equilibrium propagation. Through simulations, we obtain a test recognition accuracy of 97.6% on the MNIST handwritten digits dataset (Mixed National Institute of Standards and Technology), similar to rate-based equilibrium propagation, and comparing favorably to alternative learning techniques for spiking neural networks. We show that EqSpike implemented in silicon neuromorphic technology could reduce the energy consumption of inference and training, respectively, by three orders and two orders of magnitude compared to graphics processing units. Finally, we also show that during learning, EqSpike weight updates exhibit a form of spike-timing-dependent plasticity, highlighting a possible connection with biology.

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“…Camuñas-Mesa, Linares-Barranco and Serrano-Gotarredona focus their review on the implementation of SNNs with hybrid memristor-CMOS hardware, and review the basics of neuromorphic computing and its CMOS implementation [17]. Milo, Malavena, Monzio Compagnoli and Ielmini, mainly focus on memristive devices implementing electronic synapses in neuromorphic circuits [18]. They consider different memory technologies for brain-inspired systems including mainstream flash memory technologies, and memristive technologies with 2T and 3T structures.…”

Section: Synopsismentioning

confidence: 99%

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

Miranda

Suñé

2020

Materials

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Artificial Intelligence has found many applications in the last decade due to increased computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses in the so-called Deep Neural Networks (DNNs). Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. As far as the training is concerned, we can distinguish between supervised and unsupervised learning. The former requires labelled data and is based on the iterative minimization of the output error using the stochastic gradient descent method followed by the recalculation of the strength of the synaptic connections (weights) with the backpropagation algorithm. On the other hand, unsupervised learning does not require data labeling and it is not based on explicit output error minimization. Conventional ANNs can function with supervised learning algorithms (perceptrons, multi-layer perceptrons, convolutional networks, etc.) but also with unsupervised learning rules (Kohonen networks, self-organizing maps, etc.). Besides, another type of neural networks are the so-called Spiking Neural Networks (SNNs) in which learning takes place through the superposition of voltage spikes launched by the neurons. Their behavior is much closer to the brain functioning mechanisms they can be used with supervised and unsupervised learning rules. Since learning and inference is based on short voltage spikes, energy efficiency improves substantially. Up to this moment, all these ANNs (spiking and conventional) have been implemented as software tools running on conventional computing units based on the von Neumann architecture. However, this approach reaches important limits due to the required computing power, physical size and energy consumption. This is particularly true for applications at the edge of the internet. Thus, there is an increasing interest in developing AI tools directly implemented in hardware for this type of applications. The first hardware demonstrations have been based on Complementary Metal-Oxide-Semiconductor (CMOS) circuits and specific communication protocols. However, to further increase training speed andenergy efficiency while reducing the system size, the combination of CMOS neuron circuits with memristor synapses is now being explored. It has also been pointed out that the short time non-volatility of some memristors may even allow fabricating purely memristive ANNs. The memristor is a new device (first demonstrated in solid-state in 2008) which behaves as a resistor with memory and which has been shown to have potentiation and depression properties similar to those of biological synapses. In this Special Issue, we explore the state of the art of neuromorphic circuits implementing neural networks with memristors for AI applications.

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Memristive and CMOS Devices for Neuromorphic Computing

Cited by 103 publications

References 189 publications

Neuromorphic Motion Detection and Orientation Selectivity by Volatile Resistive Switching Memories

Neuromorphic Motion Detection and Orientation Selectivity by Volatile Resistive Switching Memories

EqSpike: Spike-driven equilibrium propagation for neuromorphic implementations

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

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