Nowadays, neuromorphic systems based on memristors are considered promising approaches to the hardware realization of artificial intelligence systems with efficient information processing. However, a ...major bottleneck in the physical implementation of these systems is the strong dependence of their performance on the unavoidable variations (cycle‐to‐cycle, c2c, or device‐to‐device, d2d) of memristive devices. Recently, reservoir computing (RC) and spiking neuromorphic systems (SNSs) are separately proposed as valuable options to partially mitigate this problem. Herein, both approaches are combined to create a fully organic system based on 1) volatile polyaniline memristive devices for the reservoir layer and 2) nonvolatile parylene memristors for the SNS readout layer. This combination provides a simpler SNS training procedure compared with the formal neural networks and results in greater robustness to device variability, while ensuring the extraction and encoding of the input critical features (performed by the polyaniline reservoir) and the analysis and classification performed by the SNS layer. Furthermore, the spatiotemporal pattern recognition of the system brings us closer to the implementation of efficient and reliable brain‐inspired computing systems built with partially unreliable analog elements.
Reservoir computing systems based on memristors are considered promising for efficient processing of temporal and dynamic data. A fully organic system with volatile polyaniline memristive devices for reservoir and nonvolatile parylene ones for spiking readout layer is presented. This system provides a simpler training procedure and greater robustness to device variability in comparison with conventional formal neural networks.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
MLP-Mixer based on multilayer perceptrons (MLPs) is a novel architecture of a neuromorphic computing system (NCS) introduced for image classification tasks without convolutional layers. Its software ...realization demonstrates high classification accuracy, although the number of trainable weights is relatively low. One more promising way of improving the NCS performance, especially in terms of power consumption, is its hardware realization using memristors. Therefore, in this work, we proposed an NCS with an adapted MLP-Mixer architecture and memristive weights. For this purpose, we used a passive crossbar array of (Co-Fe-B)
(LiNbO
)
memristors. Firstly, we studied the characteristics of such memristors, including their minimal resistive switching time, which was extrapolated to be in the picosecond range. Secondly, we created a fully hardware NCS with memristive weights that are capable of classification of simple 4-bit vectors. The system was shown to be robust to noise introduction in the input patterns. Finally, we used experimental memristive characteristics to simulate an adapted MLP-Mixer architecture that demonstrated a classification accuracy of (94.7 ± 0.3)% on the Modified National Institute of Standards and Technology (MNIST) dataset. The obtained results are the first steps toward the realization of memristive NCS with a promising MLP-Mixer architecture.
Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer ...critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.
Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer ...critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)sub.x(LiNbOsub.3)sub.100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.
Neuromorphic networks adopt human brain mechanisms and create a promising way to solve various artificial cognitive problems. Memristors, novel circuit design elements, could play the role of ...hardware synapses in such systems. Memristors based on parylene, safe material for usage within the human body, have already shown their eligibility for neuromorphic applications. The embedding of metal nanoparticles into the parylene layer leads to even better memristive characteristics, but the usage of such memristors in neuromorphic systems is still questionable. In this paper, the characteristics of nanocomposite poly(chloro-p-xylylene) memristors are studied, and the possibility of their training following spike-timing-dependent plasticity rules is demonstrated. Furthermore, a simple spiking neuromorphic network based on such memristors is successfully simulated to solve the task of breast cancer data classification. The results pave the way towards bio-compatible and energy-efficient wearable neuromorphic systems based on parylene memristors.
Display omitted
•Parylene-C memristors with embedded Ag nanoparticles are studied for the first time.•These devices demonstrate suitable for neuromorphic applications characteristics.•Studied memristors support bio-inspired STDP learning rules.•Simulated memristive spiking network demonstrates decent classification accuracy.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Memristors are promising candidates for synapse emulation in brain-inspired neuromorphic computing systems. The main obstacle to their usage in such systems is high variability of memristive ...characteristics and its severe negative effect on the neural network training. This paper addresses the issue from two points of view on the example of the parylene-based memristors: (i) the methods of the memristor internal stochasticity decrease and (ii) the methods of the memristive neural network architecture simplification. The introduction of an optimal Ag nanoparticle concentration (3 vol.%–6 vol.%) to the memristive structure leads to a statistically significant decrease in the switching voltage variation and endurance increase. Moreover, it is shown that post-fabrication annealing improves memristive characteristics, e.g., resistive switching window increases by an order of magnitude and exceeds 10
6
, the switching voltage variation decreases by a factor of 2 (down to 7% for the set and 17% for the reset voltage), and thermostability is improved. Additional transmission electron microscopy and impedance spectroscopy analysis allowed establishing a multifilamentary resistive switching mechanism for nanocomposite parylene-based memristors. The simulation of the formal neural network based on these memristors demonstrates high classification accuracy with low variation for an important biomedical task, heart disease prediction, after careful feature selection and network architecture simplification. Future prospects of the controlled incorporation of the nanocomposite parylene-based memristors in neural networks are brightened by their scaling possibility in crossbar geometry.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
MLP-Mixer based on multilayer perceptrons (MLPs) is a novel architecture of a neuromorphic computing system (NCS) introduced for image classification tasks without convolutional layers. Its software ...realization demonstrates high classification accuracy, although the number of trainable weights is relatively low. One more promising way of improving the NCS performance, especially in terms of power consumption, is its hardware realization using memristors. Therefore, in this work, we proposed an NCS with an adapted MLP-Mixer architecture and memristive weights. For this purpose, we used a passive crossbar array of (Co-Fe-B)
x
(LiNbO
3
)
100−
x
memristors. Firstly, we studied the characteristics of such memristors, including their minimal resistive switching time, which was extrapolated to be in the picosecond range. Secondly, we created a fully hardware NCS with memristive weights that are capable of classification of simple 4-bit vectors. The system was shown to be robust to noise introduction in the input patterns. Finally, we used experimental memristive characteristics to simulate an adapted MLP-Mixer architecture that demonstrated a classification accuracy of (94.7 ± 0.3)% on the Modified National Institute of Standards and Technology (MNIST) dataset. The obtained results are the first steps toward the realization of memristive NCS with a promising MLP-Mixer architecture.
MLP-Mixer neuromorphic network based on nanocomposite memristive synapses has been developed for efficient and robust classification of images.
