Artificial neuron operations and spike-timing-dependent plasticity using memristive devices for brain-inspired computing

Marukame, Takao; Nishi, Yoshifumi; Yasuda, Shinichi; Tanamoto, Tetsufumi

doi:10.7567/jjap.57.04fk06

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…31,33,34) Variations such as process, supply voltage, and temperature (PVT) do not critically deteriorate the computation precision because of our differential signaling. It was also confirmed that the NN algorithm itself is inherently tolerant to variations of electrical parameters, 5,25,35) therefore, this motivates us to further involve emerging technologies after our prototyping in this study.…”

Section: Performance Evaluation and Discussionsupporting

confidence: 58%

Integrated analog neurons inspired by mimicking synapses with metal-oxide memristive devices

Marukame

Sugino

Kitamura

et al. 2020

Jpn. J. Appl. Phys.

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Artificial neural networks (NNs) integrated with emerging devices are one promising approach to overcoming the limitations of artificial intelligence hardware based on existing CMOS technologies. Two-terminal memristive devices consisting of metal-oxide WOx/MgO were fabricated and investigated in order to add synaptic memory functions to NN hardware. The device showed analog conductance changes similar to a biological synapse’s spike-timing dependent plasticity as well as its binarized characteristics, which depend on waveforms of voltage spikes. To exploit such properties of the memristive devices for practical hardware, NN simulations were executed taking into account analog and binary synapses, resulting in good accuracy. For rapid NN prototyping with resistive synapses, we developed an analog-to-digital mixed circuit with variable resistors, which was fabricated using a standard CMOS process. Thirty-two analog neurons, where 1 neuron comprises 32 synapses, performed 1k synaptic operations at approximately 10 mW and generate neuronal firing at approximately less than 2 μs.

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Section: Performance Evaluation and Discussionsupporting

confidence: 58%

Integrated analog neurons inspired by mimicking synapses with metal-oxide memristive devices

Marukame

Sugino

Kitamura

et al. 2020

Jpn. J. Appl. Phys.

Self Cite

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“…Controlling the resistances of a population of memristors in an analogue fashion, however, is not an easy task. Because of the uncontrollable variability, careful pulse tuning is required for each device to reproduce its designed behaviour 18 , hindering practical use of memristors for synaptic devices.…”

Section: Introductionmentioning

confidence: 99%

Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity

Nishi

Nomura

Marukame

et al. 2021

Sci Rep

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Spike timing-dependent plasticity (STDP), which is widely studied as a fundamental synaptic update rule for neuromorphic hardware, requires precise control of continuous weights. From the viewpoint of hardware implementation, a simplified update rule is desirable. Although simplified STDP with stochastic binary synapses was proposed previously, we find that it leads to degradation of memory maintenance during learning, which is unfavourable for unsupervised online learning. In this work, we propose a stochastic binary synaptic model where the cumulative probability of the weight change evolves in a sigmoidal fashion with potentiation or depression trials, which can be implemented using a pair of switching devices consisting of serially connected multiple binary memristors. As a benchmark test we perform simulations of unsupervised learning of MNIST images with a two-layer network and show that simplified STDP in combination with this model can outperform conventional rules with continuous weights not only in memory maintenance but also in recognition accuracy. Our method achieves 97.3% in recognition accuracy, which is higher than that reported with standard STDP in the same framework. We also show that the high performance of our learning rule is robust against device-to-device variability of the memristor's probabilistic behaviour.

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“…5,6) In order to meet the challenges posed by the above-mentioned problems, researchers are trying to find breakthroughs from multiple angles. As one of the solutions, brain-inspired computing, [7][8][9] such as artificial neural networks (ANNs), [9][10][11] is widely favored as a promising computing paradigm. [12][13][14] Deep neural networks (DNNs), one of the ANNs, are widely used in pattern recognition, language translation, visual object recognition, object detection, and many other fields.…”

Section: Introductionmentioning

confidence: 99%

An ultra-compact leaky integrate-and-fire neuron with long and tunable time constant utilizing pseudo resistors for spiking neural networks

Chen¹,

Yajima²,

Inoue³

et al. 2022

Jpn. J. Appl. Phys.

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Spiking neural networks (SNNs) inspired by biological neurons enable a more realistic mimicry of the human brain. To realize SNNs similar to large-scale biological networks, neuron circuits with high area efficiency are essential. In this paper, we propose a compact leaky integrate-and-fire (LIF) neuron circuit with a long and tunable time constant, which consists of a capacitor and two pseudo resistors (PRs). The prototype chip was fabricated with TSMC 65 nm CMOS technology, and it occupies a die area of 1392 μm2. The fabricated LIF neuron has a power consumption of 6 μW and a leak time constant of up to 1.2 ms (the resistance of PR is up to 600 MΩ). In addition, the time constants are tunable by changing the bias voltage of PRs. Overall, this proposed neuron circuit facilitates the very-large-scale integration (VLSI) of adaptive SNNs, which is crucial for the implementation of bio-scale brain-inspired computing.

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Artificial neuron operations and spike-timing-dependent plasticity using memristive devices for brain-inspired computing

Cited by 6 publications

References 31 publications

Integrated analog neurons inspired by mimicking synapses with metal-oxide memristive devices

Integrated analog neurons inspired by mimicking synapses with metal-oxide memristive devices

Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity

An ultra-compact leaky integrate-and-fire neuron with long and tunable time constant utilizing pseudo resistors for spiking neural networks

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