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Das H, Schuman C, Chakraborty NN, Rose GS. Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks. Sci Rep 2024; 14:8897. [PMID: 38632304 PMCID: PMC11024114 DOI: 10.1038/s41598-024-58947-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 04/04/2024] [Indexed: 04/19/2024] Open
Abstract
The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the HfO 2 based memristive device. 1 st and 2 nd stage device of our proposed synapse design can be scaled to enhance the READ current margin up to ∼ 4.3 × and ∼ 21%, respectively. Moreover, READ current resolution can be enhanced with run-time adaptation techniques such as READ voltage scaling and body biasing. The READ voltage scaling and body biasing can improve the READ current resolution by about 46% and 15%, respectively. TENNLab's neuromorphic computing framework is leveraged to evaluate the effect of READ current resolution on classification, control, and reservoir computing applications. Higher READ current resolution shows better accuracy than lower resolution even when facing different levels of read noise.
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Affiliation(s)
- Hritom Das
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA.
| | - Catherine Schuman
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA
| | - Nishith N Chakraborty
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA
| | - Garrett S Rose
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA
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2
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Gemo E, Spiga S, Brivio S. SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks. Front Neurosci 2024; 17:1270090. [PMID: 38264497 PMCID: PMC10804805 DOI: 10.3389/fnins.2023.1270090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/14/2023] [Indexed: 01/25/2024] Open
Abstract
Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.
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Affiliation(s)
- Emanuele Gemo
- CNR–IMM, Unit of Agrate Brianza, Agrate Brianza, Italy
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3
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Gebicke-Haerter PJ. The computational power of the human brain. Front Cell Neurosci 2023; 17:1220030. [PMID: 37608987 PMCID: PMC10441807 DOI: 10.3389/fncel.2023.1220030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/05/2023] [Indexed: 08/24/2023] Open
Abstract
At the end of the 20th century, analog systems in computer science have been widely replaced by digital systems due to their higher computing power. Nevertheless, the question keeps being intriguing until now: is the brain analog or digital? Initially, the latter has been favored, considering it as a Turing machine that works like a digital computer. However, more recently, digital and analog processes have been combined to implant human behavior in robots, endowing them with artificial intelligence (AI). Therefore, we think it is timely to compare mathematical models with the biology of computation in the brain. To this end, digital and analog processes clearly identified in cellular and molecular interactions in the Central Nervous System are highlighted. But above that, we try to pinpoint reasons distinguishing in silico computation from salient features of biological computation. First, genuinely analog information processing has been observed in electrical synapses and through gap junctions, the latter both in neurons and astrocytes. Apparently opposed to that, neuronal action potentials (APs) or spikes represent clearly digital events, like the yes/no or 1/0 of a Turing machine. However, spikes are rarely uniform, but can vary in amplitude and widths, which has significant, differential effects on transmitter release at the presynaptic terminal, where notwithstanding the quantal (vesicular) release itself is digital. Conversely, at the dendritic site of the postsynaptic neuron, there are numerous analog events of computation. Moreover, synaptic transmission of information is not only neuronal, but heavily influenced by astrocytes tightly ensheathing the majority of synapses in brain (tripartite synapse). At least at this point, LTP and LTD modifying synaptic plasticity and believed to induce short and long-term memory processes including consolidation (equivalent to RAM and ROM in electronic devices) have to be discussed. The present knowledge of how the brain stores and retrieves memories includes a variety of options (e.g., neuronal network oscillations, engram cells, astrocytic syncytium). Also epigenetic features play crucial roles in memory formation and its consolidation, which necessarily guides to molecular events like gene transcription and translation. In conclusion, brain computation is not only digital or analog, or a combination of both, but encompasses features in parallel, and of higher orders of complexity.
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Affiliation(s)
- Peter J. Gebicke-Haerter
- Institute of Psychopharmacology, Central Institute of Mental Health, Faculty of Medicine, University of Heidelberg, Mannheim, Germany
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4
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Giotis C, Serb A, Manouras V, Stathopoulos S, Prodromakis T. Palimpsest memories stored in memristive synapses. SCIENCE ADVANCES 2022; 8:eabn7920. [PMID: 35731877 PMCID: PMC9217086 DOI: 10.1126/sciadv.abn7920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Biological synapses store multiple memories on top of each other in a palimpsest fashion and at different time scales. Palimpsest consolidation is facilitated by the interaction of hidden biochemical processes governing synaptic efficacy during varying lifetimes. This arrangement allows idle memories to be temporarily overwritten without being forgotten, while previously unseen memories are used in the short term. While embedded artificial intelligence can greatly benefit from this functionality, a practical demonstration in hardware is missing. Here, we show how the intrinsic properties of metal-oxide volatile memristors emulate the processes supporting biological palimpsest consolidation. Our memristive synapses exhibit an expanded doubled capacity and protect a consolidated memory while up to hundreds of uncorrelated short-term memories temporarily overwrite it, without requiring specialized instructions. We further demonstrate this technology in the context of visual working memory. This showcases how emerging memory technologies can efficiently expand the capabilities of artificial intelligence hardware toward more generalized learning memories.
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Affiliation(s)
- Christos Giotis
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Alexander Serb
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
- Centre for Electronics Frontiers, School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK
| | - Vasileios Manouras
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Spyros Stathopoulos
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Themis Prodromakis
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
- Centre for Electronics Frontiers, School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK
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5
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Lee C, Noh K, Ji W, Gokmen T, Kim S. Impact of Asymmetric Weight Update on Neural Network Training With Tiki-Taka Algorithm. Front Neurosci 2022; 15:767953. [PMID: 35069098 PMCID: PMC8770851 DOI: 10.3389/fnins.2021.767953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Recent progress in novel non-volatile memory-based synaptic device technologies and their feasibility for matrix-vector multiplication (MVM) has ignited active research on implementing analog neural network training accelerators with resistive crosspoint arrays. While significant performance boost as well as area- and power-efficiency is theoretically predicted, the realization of such analog accelerators is largely limited by non-ideal switching characteristics of crosspoint elements. One of the most performance-limiting non-idealities is the conductance update asymmetry which is known to distort the actual weight change values away from the calculation by error back-propagation and, therefore, significantly deteriorates the neural network training performance. To address this issue by an algorithmic remedy, Tiki-Taka algorithm was proposed and shown to be effective for neural network training with asymmetric devices. However, a systematic analysis to reveal the required asymmetry specification to guarantee the neural network performance has been unexplored. Here, we quantitatively analyze the impact of update asymmetry on the neural network training performance when trained with Tiki-Taka algorithm by exploring the space of asymmetry and hyper-parameters and measuring the classification accuracy. We discover that the update asymmetry level of the auxiliary array affects the way the optimizer takes the importance of previous gradients, whereas that of main array affects the frequency of accepting those gradients. We propose a novel calibration method to find the optimal operating point in terms of device and network parameters. By searching over the hyper-parameter space of Tiki-Taka algorithm using interpolation and Gaussian filtering, we find the optimal hyper-parameters efficiently and reveal the optimal range of asymmetry, namely the asymmetry specification. Finally, we show that the analysis and calibration method be applicable to spiking neural networks.
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Affiliation(s)
- Chaeun Lee
- Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang-si, South Korea
| | - Kyungmi Noh
- Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang-si, South Korea
| | - Wonjae Ji
- Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang-si, South Korea
| | - Tayfun Gokmen
- IBM Research AI, Yorktown Heights, NY, United States
| | - Seyoung Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang-si, South Korea
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6
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Verma G, Bindal N, Nisar A, Dhull S, Kaushik BK. Advances in Neuromorphic Spin-Based Spiking Neural Networks: A review. IEEE NANOTECHNOLOGY MAGAZINE 2021. [DOI: 10.1109/mnano.2021.3098219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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7
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Zhang Y, Wu Z, Liu S, Guo Z, Chen Q, Gao P, Wang P, Liu G. A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition. Front Neurosci 2021; 15:717222. [PMID: 34602968 PMCID: PMC8481819 DOI: 10.3389/fnins.2021.717222] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 08/12/2021] [Indexed: 11/17/2022] Open
Abstract
The interference of noise will cause the degradation of image quality, which can have a negative impact on the subsequent image processing and visual effect. Although the existing image denoising algorithms are relatively perfect, their computational efficiency is restricted by the performance of the computer, and the computational process consumes a lot of energy. In this paper, we propose a method for image denoising and recognition based on multi-conductance states of memristor devices. By regulating the evolution of Pt/ZnO/Pt memristor wires, 26 continuous conductance states were obtained. The image feature preservation and noise reduction are realized via the mapping between the conductance state and the image pixel. Furthermore, weight quantization of convolutional neural network is realized based on multi-conductance states. The simulation results show the feasibility of CNN for image denoising and recognition based on multi-conductance states. This method has a certain guiding significance for the construction of high-performance image noise reduction hardware system.
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Affiliation(s)
- Yuejun Zhang
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China
| | - Zhixin Wu
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shuzhi Liu
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhecheng Guo
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China
| | - Qilai Chen
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
- School of Materials, Sun Yat-sen University, Guangzhou, China
| | - Pingqi Gao
- School of Materials, Sun Yat-sen University, Guangzhou, China
| | - Pengjun Wang
- College of Mathematics, Physics, and Electronic Information Engineering, Wenzhou University, Wenzhou, China
| | - Gang Liu
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
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8
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Covi E, Donati E, Liang X, Kappel D, Heidari H, Payvand M, Wang W. Adaptive Extreme Edge Computing for Wearable Devices. Front Neurosci 2021; 15:611300. [PMID: 34045939 PMCID: PMC8144334 DOI: 10.3389/fnins.2021.611300] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 03/24/2021] [Indexed: 11/13/2022] Open
Abstract
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.
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Affiliation(s)
| | - Elisa Donati
- Institute of Neuroinformatics, University of Zurich, Eidgenössische Technische Hochschule Zürich (ETHZ), Zurich, Switzerland
| | - Xiangpeng Liang
- Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - David Kappel
- Bernstein Center for Computational Neuroscience, III Physikalisches Institut–Biophysik, Georg-August Universität, Göttingen, Germany
| | - Hadi Heidari
- Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Melika Payvand
- Institute of Neuroinformatics, University of Zurich, Eidgenössische Technische Hochschule Zürich (ETHZ), Zurich, Switzerland
| | - Wei Wang
- The Andrew and Erna Viterbi Department of Electrical Engineering, Technion–Israel Institute of Technology, Haifa, Israel
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9
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Kim T, Hu S, Kim J, Kwak JY, Park J, Lee S, Kim I, Park JK, Jeong Y. Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update. Front Comput Neurosci 2021; 15:646125. [PMID: 33776676 PMCID: PMC7996210 DOI: 10.3389/fncom.2021.646125] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 02/15/2021] [Indexed: 11/13/2022] Open
Abstract
Among many artificial neural networks, the research on Spike Neural Network (SNN), which mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor is a promising candidate as a synaptic component for hardware implementation of SNN, but several non-ideal device properties are making it challengeable. In this work, we conducted an SNN simulation by adding a device model with a non-linear weight update to test the impact on SNN performance. We found that SNN has a strong tolerance for the device non-linearity and the network can keep the accuracy high if a device meets one of the two conditions: 1. symmetric LTP and LTD curves and 2. positive non-linearity factors for both LTP and LTD. The reason was analyzed in terms of the balance between network parameters as well as the variability of weight. The results are considered to be a piece of useful prior information for the future implementation of emerging device-based neuromorphic hardware.
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Affiliation(s)
| | | | | | | | | | | | | | | | - YeonJoo Jeong
- Center for Neuromorphic Engineering, Korea Institutes of Science and Technology, Seoul, South Korea
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10
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Tiotto TF, Goossens AS, Borst JP, Banerjee T, Taatgen NA. Learning to Approximate Functions Using Nb-Doped SrTiO 3 Memristors. Front Neurosci 2021; 14:627276. [PMID: 33679290 PMCID: PMC7933504 DOI: 10.3389/fnins.2020.627276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 12/24/2020] [Indexed: 11/27/2022] Open
Abstract
Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses. We performed measurements on interface-type memristors to validate their use in neuromorphic hardware. Specifically, we utilized Nb-doped SrTiO3 memristors as synapses in a simulated neural network by arranging them into differential synaptic pairs, with the weight of the connection given by the difference in normalized conductance values between the two paired memristors. This network learned to represent functions through a training process based on a novel supervised learning algorithm, during which discrete voltage pulses were applied to one of the two memristors in each pair. To simulate the fact that both the initial state of the physical memristive devices and the impact of each voltage pulse are unknown we injected noise into the simulation. Nevertheless, discrete updates based on local knowledge were shown to result in robust learning performance. Using this class of memristive devices as the synaptic weight element in a spiking neural network yields, to our knowledge, one of the first models of this kind, capable of learning to be a universal function approximator, and strongly suggests the suitability of these memristors for usage in future computing platforms.
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Affiliation(s)
- Thomas F. Tiotto
- Groningen Cognitive Systems and Materials Center, University of Groningen, Groningen, Netherlands
- Artificial Intelligence, Bernoulli Institute, University of Groningen, Groningen, Netherlands
| | - Anouk S. Goossens
- Groningen Cognitive Systems and Materials Center, University of Groningen, Groningen, Netherlands
- Zernike Institute for Advanced Materials, University of Groningen, Groningen, Netherlands
| | - Jelmer P. Borst
- Groningen Cognitive Systems and Materials Center, University of Groningen, Groningen, Netherlands
- Artificial Intelligence, Bernoulli Institute, University of Groningen, Groningen, Netherlands
| | - Tamalika Banerjee
- Groningen Cognitive Systems and Materials Center, University of Groningen, Groningen, Netherlands
- Zernike Institute for Advanced Materials, University of Groningen, Groningen, Netherlands
| | - Niels A. Taatgen
- Groningen Cognitive Systems and Materials Center, University of Groningen, Groningen, Netherlands
- Artificial Intelligence, Bernoulli Institute, University of Groningen, Groningen, Netherlands
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11
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Brivio S, Ly DRB, Vianello E, Spiga S. Non-linear Memristive Synaptic Dynamics for Efficient Unsupervised Learning in Spiking Neural Networks. Front Neurosci 2021; 15:580909. [PMID: 33633531 PMCID: PMC7901913 DOI: 10.3389/fnins.2021.580909] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 01/06/2021] [Indexed: 11/13/2022] Open
Abstract
Spiking neural networks (SNNs) are a computational tool in which the information is coded into spikes, as in some parts of the brain, differently from conventional neural networks (NNs) that compute over real-numbers. Therefore, SNNs can implement intelligent information extraction in real-time at the edge of data acquisition and correspond to a complementary solution to conventional NNs working for cloud-computing. Both NN classes face hardware constraints due to limited computing parallelism and separation of logic and memory. Emerging memory devices, like resistive switching memories, phase change memories, or memristive devices in general are strong candidates to remove these hurdles for NN applications. The well-established training procedures of conventional NNs helped in defining the desiderata for memristive device dynamics implementing synaptic units. The generally agreed requirements are a linear evolution of memristive conductance upon stimulation with train of identical pulses and a symmetric conductance change for conductance increase and decrease. Conversely, little work has been done to understand the main properties of memristive devices supporting efficient SNN operation. The reason lies in the lack of a background theory for their training. As a consequence, requirements for NNs have been taken as a reference to develop memristive devices for SNNs. In the present work, we show that, for efficient CMOS/memristive SNNs, the requirements for synaptic memristive dynamics are very different from the needs of a conventional NN. System-level simulations of a SNN trained to classify hand-written digit images through a spike timing dependent plasticity protocol are performed considering various linear and non-linear plausible synaptic memristive dynamics. We consider memristive dynamics bounded by artificial hard conductance values and limited by the natural dynamics evolution toward asymptotic values (soft-boundaries). We quantitatively analyze the impact of resolution and non-linearity properties of the synapses on the network training and classification performance. Finally, we demonstrate that the non-linear synapses with hard boundary values enable higher classification performance and realize the best trade-off between classification accuracy and required training time. With reference to the obtained results, we discuss how memristive devices with non-linear dynamics constitute a technologically convenient solution for the development of on-line SNN training.
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Affiliation(s)
- Stefano Brivio
- CNR - IMM, Unit of Agrate Brianza, Agrate Brianza, Italy
| | - Denys R B Ly
- Université Grenoble Alpes, CEA, Leti, Grenoble, France
| | | | - Sabina Spiga
- CNR - IMM, Unit of Agrate Brianza, Agrate Brianza, Italy
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12
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Emelyanov AV, Nikiruy KE, Serenko AV, Sitnikov AV, Presnyakov MY, Rybka RB, Sboev AG, Rylkov VV, Kashkarov PK, Kovalchuk MV, Demin VA. Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights. NANOTECHNOLOGY 2020; 31:045201. [PMID: 31578002 DOI: 10.1088/1361-6528/ab4a6d] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above. Different kinds of local rules for learning spiking systems are mostly realized on a bio-inspired spike-time-dependent plasticity (STDP) mechanism, which is an improved type of classical Hebbian learning. Whereas the STDP-like mechanism has already been shown to emerge naturally in memristive devices, the demonstration of its self-adaptive learning property, potentially overcoming the variability problem, is more challenging and has yet to be reported. Here we experimentally demonstrate an STDP-based learning protocol that ensures self-adaptation of the memristor resistive states, after only a very few spikes, and makes the plasticity sensitive only to the input signal configuration, but neither to the initial state of the devices nor their device-to-device variability. Then, it is shown that the self-adaptive learning of a spiking neuron with memristive weights on rate-coded patterns could also be realized with hardware-based STDP rules. The experiments have been carried out with nanocomposite-based (Co40Fe40B20) х (LiNbO3-y )100-х memristive structures, but their results are believed to be applicable to a wide range of memristive devices. All the experimental data were supported and extended by numerical simulations. There is a hope that the obtained results pave the way for building up reliable spiking neuromorphic systems composed of partially unreliable analog memristive elements, with a more complex architecture and the capability of unsupervised learning.
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Affiliation(s)
- A V Emelyanov
- National Research Center 'Kurchatov Institute', 123182 Moscow, Russia. Moscow Institute of Physics and Technology (State University), 141700 Dolgoprudny, Moscow Region, Russia
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13
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Stimulated Ionic Telegraph Noise in Filamentary Memristive Devices. Sci Rep 2019; 9:6310. [PMID: 30988321 PMCID: PMC6465356 DOI: 10.1038/s41598-019-41497-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 02/25/2019] [Indexed: 11/18/2022] Open
Abstract
Random telegraph noise is a widely investigated phenomenon affecting the reliability of the reading operation of the class of memristive devices whose operation relies on formation and dissolution of conductive filaments. The trap and the release of electrons into and from defects surrounding the filament produce current fluctuations at low read voltages. In this work, telegraphic resistance variations are intentionally stimulated through pulse trains in HfO2-based memristive devices. The stimulated noise results from the re-arrangement of ionic defects constituting the filament responsible for the switching. Therefore, the stimulated noise has an ionic origin in contrast to the electronic nature of conventional telegraph noise. The stimulated noise is interpreted as raising from a dynamic equilibrium establishing from the tendencies of ionic drift and diffusion acting on the edges of conductive filament. We present a model that accounts for the observed increase of noise amplitude with the average device resistance. This work provides the demonstration and the physical foundation for the intentional stimulation of ionic telegraph noise which, on one hand, affects the programming operations performed with trains of identical pulses, as for neuromorphic computing, and on the other hand, it can open opportunities for applications relying on stochastic processes in nanoscaled devices.
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