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Spiking Neural Computing in Memristive Neuromorphic Platforms
[chapter]
2019
Handbook of Memristor Networks
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Neuromorphic computation using Spiking Neural Networks (SNN) is pro-1 posed as an alternative solution for future of computation to conquer the memory 2 bottelneck issue in recent computer architecture ...
The hardware platform is suitable to model the large-scale 83 spiking neural networks in biological real time. ...
E D P R O O F
Spiking Neural Computing in Memristive Neuromorphic Platforms
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249
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251
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253
Convolutional neural networks which have been explored intensively within the
254 ...
doi:10.1007/978-3-319-76375-0_25
fatcat:eloawfklyrgvljms6z2qg27jiq
Recent trends in neuromorphic engineering
2016
Big Data Analytics
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Neuromorphic Engineering has emerged as an exciting research area, primarily owing to the paradigm shift from conventional computing architectures to data-driven, cognitive computing. ...
There is a diversity of work in the literature pertaining to neuromorphic systems, devices and circuits. ...
., is a neuromorphic hardware co-processor for spiking neural networks on 180nm CMOS technology. ...
doi:10.1186/s41044-016-0013-1
fatcat:oyjitbviy5cdpesqwo6fp5tpgu
CMOS and Memristive Hardware for Neuromorphic Computing
2020
Advanced Intelligent Systems
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Saber Moradi and Professor Indiveri for designing the circuit in Figure 3a and the chip, where its architecture is shown in Figure 11a . ...
Figure 2 and conduct the experiments shown in Figure 11 . ...
computing platforms. ...
doi:10.1002/aisy.201900189
fatcat:lrspxweqlfb6bmmqir6mwzvx44
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
2020
IEEE Transactions on Biomedical Circuits and Systems
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With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and ...
In addition, we benchmark various hardware platforms by performing a biomedical electromyography (EMG) signal processing task and drawing comparisons among them in terms of inference delay and energy. ...
The platforms used for each system in Table V are as follows: ODIN+MorphIC [139] , [140] and Loihi [138] neuromorphic platforms were used for spiking implementations; NVIDIA Jetson Nano was used ...
doi:10.1109/tbcas.2020.3036081
pmid:33156792
fatcat:rjwfjd7vmvglpk762mqeyiteqq
Adaptive Extreme Edge Computing for Wearable Devices
2021
Frontiers in Neuroscience
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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 propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. ...
Stefan Slesazeck for useful discussion on ferroelectric and memristive devices. ...
doi:10.3389/fnins.2021.611300
pmid:34045939
pmcid:PMC8144334
fatcat:5by77im5crcslgt7zj3wulzd5e
Novel hardware and concepts for unconventional computing
2020
Scientific Reports
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is to provide a platform for interdisciplinary research along three main lines: memristive materials and devices, emulation of cellular learning (neurons and synapses), and unconventional computing and ...
neuromorphic systems are currently experiencing a rapid upswing due to the fact that today's cMoS (complementary metal oxide silicon) based technologies are increasingly approaching their limits. in particular ...
This Guest Edited Collection tries to bundle work in three main thematic areas of neuromorphic computing: memristive materials and devices, emulation of cellular forms of memory and learning, and neuromorphic ...
doi:10.1038/s41598-020-68834-1
pmid:32678249
fatcat:2zrgtglk7rd4zg5boykrvu7lji
Low-Power Neuromorphic Hardware for Signal Processing Applications
[article]
2019
arXiv
pre-print
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This is unlike the neurons in the human brain, which encode and process information using temporal information in spike events. ...
Inspired by the time-encoding mechanism used by the brain, third generation spiking neural networks (SNNs) are being studied for building a new class of information processing engines. ...
large neuromorphic platforms designed using ultra-low-power computational memories that leverage nanoscale memristive technologies. ...
arXiv:1901.03690v3
fatcat:34eavryprvdaxcvuteujwizeia
Neuromorphic Electronic Systems for Reservoir Computing
[article]
2020
arXiv
pre-print
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Due to its computational efficiency and the fact that training amounts to a simple linear regression, both spiking and non-spiking implementations of reservoir computing on neuromorphic hardware have been ...
Moreover, to deal with challenges of computation on such unconventional substrates, several lines of potential solutions are presented based on advances in other computational approaches in machine learning ...
One of the key components in both digital and analog neuromorphic reservoir computers is, therefore, a neural encoder which transforms the input signals into the spike trains. ...
arXiv:1908.09572v2
fatcat:cimkbnvyrjc3lhixlyufgmqy3i
Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
2019
Materials
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However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. ...
Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. ...
Most of the developed large-scale CMOS neuromorphic computing platforms make use of this AER communication protocol. ...
doi:10.3390/ma12172745
pmid:31461877
pmcid:PMC6747825
fatcat:bt6hgscpczd2ldc4xc6np7wsvu
Impact of increasing number of neurons on performance of neuromorphic architecture
2017
2017 19th International Symposium on Computer Architecture and Digital Systems (CADS)
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The spike neural networks inspired from physiological brain architecture, is a neuromorphic hardware implementation of network of neurons. ...
The goal of this paper is performance evaluation of neuromorphic architecture in terms of recognition rates using different numbers of output neurons. ...
ACKNOWLEDGMENTS This work has been supported by European Network on High Performance and Embedded Architecture and Compilation (HiPEAC) in collaboration grant agreement H2020-687698. ...
doi:10.1109/cads.2017.8310732
fatcat:a2g5v4kx4rhsjemhq6d2rmjctq
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
[article]
2020
arXiv
pre-print
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With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and ...
In addition, we benchmark various hardware platforms by performing a biomedical electromyography (EMG) signal processing task and drawing comparisons among them in terms of inference delay and energy. ...
The platforms used for each system in Table V are as follows: ODIN+MorphIC [117] , [118] and Loihi [116] neuromorphic platforms were used for spiking implementations; NVIDIA Jetson Nano was used ...
arXiv:2007.05657v1
fatcat:amqutl3suvgq5nygna4ef36usy
Energy-Efficient CMOS Memristive Synapses for Mixed-Signal Neuromorphic System-on-a-Chip
[article]
2018
arXiv
pre-print
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The proposed mixed-signal memristive synapse can be designed and fabricated using standard CMOS technologies and open doors to interesting applications in cognitive computing circuits. ...
Even though several algorithmic challenges need to be addressed to turn the vision of memristive Neuromorphic Systems-on-a-Chip (NeuSoCs) into reality, issues at the device and circuit interface need immediate ...
Index Terms-CMOS Neurons, Memristors, Neuromorphic computing, Spiking Neural Networks (SNNs), STDP, Synapses.
I. ...
arXiv:1802.02342v3
fatcat:viufks3eobgf7ijxwttq5xcpce
Perspective on photonic memristive neuromorphic computing
2020
PhotoniX
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Neuromorphic computing applies concepts extracted from neuroscience to develop devices shaped like neural systems and achieve brain-like capacity and efficiency. ...
In this Perspective, we review the rapid development of the neuromorphic computing field both in the electronic and in the photonic domain focusing on the role and the applications of memristors. ...
MG conceived the present idea, QZ performed the calculations reported in Fig. 10 and EG wrote the paper with input from all authors. All authors read and approved the final manuscript. ...
doi:10.1186/s43074-020-0001-6
fatcat:qvhwulx7ova4lnijjhtwnpunqe
STDP implementation using memristive nanodevice in CMOS-Nano neuromorphic networks
2009
IEICE Electronics Express
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Implementation of a correlation-based learning rule, Spike-Timing-Dependent-Plasticity (STDP), for asynchronous neuromorphic networks is demonstrated using 'memristive' nanodevice. ...
The learning method is dynamic and online in which the synaptic weights are modified based on neural activity. ...
to adapt it to the CMOL platform, as sketched in Fig. 2 (a) . ...
doi:10.1587/elex.6.148
fatcat:hhe2lkfp3rfr5o46jiga6ccym4
Integration and co-design of memristive devices and algorithms for artificial intelligence
2020
iScience
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have been observed in biological components. ...
Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic ...
The implementation of memristive neurons has also enabled fully memristive neuromorphic computing (Wang et al., 2018) , further enhancing the integration level of the hardware neuromorphic computing. ...
doi:10.1016/j.isci.2020.101809
pmid:33305176
pmcid:PMC7718163
fatcat:bibhecux2nafzjexaklossadae
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