Drift-Enhanced Unsupervised Learning of Handwritten Digits in Spiking Neural Network With PCM Synapses

Oh, Sangheon; Shi, Yuhan; Liu, Xin; Song, Jungwoo; Kuzum, Duygu

doi:10.1109/led.2018.2872434

Cited by 33 publications

(31 citation statements)

References 18 publications

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“…The impact of the drift on the accuracy of the neural network and computing systems should be fully evaluated and the external algorithms should be implemented to hedge the drift errors in these PCM systems. [145][146][147] A complete brain-inspired system that has the function of spike-based neuromorphic computing should incorporate both neurons and synapses. Tuma et al [148] displayed a neurosynaptic correlation detector consisting of an integrate-and-fire neuron and a series of synapses, shown in orange and blue, respectively, in Figure 10a.…”

Section: (11 Of 21)mentioning

confidence: 99%

Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

Mai

Lin

et al. 2020

Adv Funct Materials

172

120

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Traditional von Neumann computing architecture with separated computation and storage units has already impeded the data processing performance and energy efficiency, calling for emerging neuromorphic electronic and optical devices and systems which can mimic the human brain to shift this paradigm. Material-level innovation has become the key component to this revolution of information technology. Chalcogenide phase-change material (PCM) as a well-acknowledged data-storage medium is a promising candidate to tackle this challenge. In this review, the use of PCMs to implement artificial neurons and synapses from both the electronic and optical respects is discussed, and in particular, the structure-property physics and transition dynamics that enable such brain-inspired and in-memory computing applications are emphasized. Recent advances on the atomic-level amorphous and crystalline structures, transition mechanisms, materials optimization and design, neural and synaptic devices, brain-inspired chips, and computing systems, as well as the future opportunities of PCMs, are summarized and discussed.

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Section: (11 Of 21)mentioning

confidence: 99%

Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

Mai

Lin

et al. 2020

Adv Funct Materials

172

120

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“…One approach could be using a single device to represent a synaptic weight. The weights in the network are linearly transformed to the conductance range as shown in Equation (6) for the hardware implementation (Serb et al, 2016; Kim et al, 2018; Li et al, 2018; Oh et al, 2018; Shi et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

A Soft-Pruning Method Applied During Training of Spiking Neural Networks for In-memory Computing Applications

Shi

Nguyen

et al. 2019

Front. Neurosci.

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Inspired from the computational efficiency of the biological brain, spiking neural networks (SNNs) emulate biological neural networks, neural codes, dynamics, and circuitry. SNNs show great potential for the implementation of unsupervised learning using in-memory computing. Here, we report an algorithmic optimization that improves energy efficiency of online learning with SNNs on emerging non-volatile memory (eNVM) devices. We develop a pruning method for SNNs by exploiting the output firing characteristics of neurons. Our pruning method can be applied during network training, which is different from previous approaches in the literature that employ pruning on already-trained networks. This approach prevents unnecessary updates of network parameters during training. This algorithmic optimization can complement the energy efficiency of eNVM technology, which offers a unique in-memory computing platform for the parallelization of neural network operations. Our SNN maintains ~90% classification accuracy on the MNIST dataset with up to ~75% pruning, significantly reducing the number of weight updates. The SNN and pruning scheme developed in this work can pave the way toward applications of eNVM based neuro-inspired systems for energy efficient online learning in low power applications.

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“…[7,8] However, because CMOS devices lack intrinsic biological resemblance, the constructed circuits are always complex, which limits the power and area efficiencies. [9] In recent years, emerging devices, e.g., resistive random-access memory, [10][11][12][13][14] phase change memory, [15][16][17][18][19] ferroelectric randomaccess memory, [20][21][22][23] van der Waals heterostructures, [24,25] and synaptic transistors, [26][27][28] have been successfully demonstrated for the emulation of biological synapses and neurons. The advantage of the emerging devices is that the synaptic or neural functions can be mimicked at least partially by a single device, greatly reducing the circuit complexity.…”

Section: Doi: 101002/adma202004398mentioning

confidence: 99%

A Habituation Sensory Nervous System with Memristors

Shi

et al. 2020

Advanced Materials

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The sensory nervous system (SNS) builds up the association between external stimuli and the response of organisms. In this system, habituation is a fundamental characteristic that filters out irrelevantly repetitive information and makes the SNS adapt to the external environment. To emulate this critical process in electronic devices, a LixSiOy‐based memristor (TiN/LixSiOy/Pt) is developed where the temporal response under repetitive stimulation is similar to that of habituation. By connecting this synaptic device to a leaky integrate‐and‐fire neuron based on a Ag/SiO2:Ag/Au memristor, a fully memristive SNS with habituation is experimentally demonstrated. Finally, a habituation spiking neural network based on the SNS is built and its application in obstacle avoidance for robot navigation is successfully presented. The results provide that a direct emulation of the biologically inspired learning process by memristors could be a sound choice for neuromorphic hardware implementation.

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Drift-Enhanced Unsupervised Learning of Handwritten Digits in Spiking Neural Network With PCM Synapses

Cited by 33 publications

References 18 publications

Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

Recent Advances on Neuromorphic Devices Based on Chalcogenide Phase‐Change Materials

A Soft-Pruning Method Applied During Training of Spiking Neural Networks for In-memory Computing Applications

A Habituation Sensory Nervous System with Memristors

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