Relaxation oscillator-realized artificial electronic neurons, their responses, and noise

Lim, Hyungkwang; Ahn, Hyung-Woo; Kornijcuk, Vladimir; Kim, Guhyun; Seok, Jun Yeong; Kim, Inho; Hwang, Cheol Seong; Jeong, Doo Seok

doi:10.1039/c6nr01278g

Cited by 43 publications

(29 citation statements)

References 59 publications

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“…Reference [142] suggested that variability may be useful for stochastic computing, although this work used a threshold device. However, it is true that variability may be exploited for the development of useful systems, such as true random number generators and physical unclonable function devices for security applications.…”

Section: Variabilitymentioning

confidence: 99%

Recommended Methods to Study Resistive Switching Devices

Lanza

Wong

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et al. 2018

Adv Elect Materials

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434

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Resistive switching (RS) is an interesting property shown by some materials systems that, especially during the last decade, has gained a lot of interest for the fabrication of electronic devices, with electronic nonvolatile memories being those that have received the most attention. The presence and quality of the RS phenomenon in a materials system can be studied using different prototype cells, performing different experiments, displaying different figures of merit, and developing different computational analyses. Therefore, the real usefulness and impact of the findings presented in each study for the RS technology will be also different. This manuscript describes the most recommendable methodologies for the fabrication, characterization, and simulation of RS devices, as well as the proper methods to display the data obtained. The idea is to help the scientific community to evaluate the real usefulness and impact of an RS study for the development of RS technology.

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Section: Variabilitymentioning

confidence: 99%

Recommended Methods to Study Resistive Switching Devices

Lanza

Wong

Pop

et al. 2018

Adv Elect Materials

525

434

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“…In PCM neurons, the as-made device consists of a nanoscale volume of phase-change material initially in the crystalline phase. [76,77] Threshold switching also occurs in RRAM devices, especially in the low operation voltage or current regime, and hence it can be exploited as volatile "turn-on" behavior, resembling the function of neurons. If the pulse is cut off abruptly, the molten part will rapidly quench into the amorphous phase following a glass transition.…”

Section: Threshold Switching In Artificial Neuronsmentioning

confidence: 99%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re‐visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

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“…A CBA of resistance-based memory is perhaps most suitable for the MCHL algorithm, which emphasizes the benefits of the MCHL algorithm for efficient matrix calculation. 5,9,26 Given the stochasticity in resistance switching (particularly, on-and off-switching voltages 27,28 ) in nature, the probabilistic weight transition may be achieved by controlling driving voltage without RN generation 29 . Additionally, every update simply overwrites the current memory contents in this training scheme in that the past weight matrix no longer needs to be kept given the Markov chain nature, which also alleviates large memory needs.…”

Section: Discussionmentioning

confidence: 99%

Markov Chain Hebbian Learning Algorithm With Ternary Synaptic Units

et al. 2019

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In spite of remarkable progress in machine learning techniques, the state-of-the-art machine learning algorithms often keep machines from real-time learning (online learning) due in part to computational complexity in parameter optimization. As an alternative, a learning algorithm to train a memory in real time is proposed, which is named as the Markov chain Hebbian learning algorithm. The algorithm pursues efficient memory use during training in that (i) the weight matrix has ternary elements (-1, 0, 1) and (ii) each update follows a Markov chainthe upcoming update does not need past weight memory. The algorithm was verified by two proof-of-concept tasks (handwritten digit recognition and multiplication table memorization) in which numbers were taken as symbols.Particularly, the latter bases multiplication arithmetic on memory, which may be analogous to humans' mental arithmetic. The memory-based multiplication arithmetic feasibly offers the basis of factorization, supporting novel insight into the arithmetic.Recent progress in machine learning (particularly, deep learning) endows artificial intelligence with high precision recognition and problem-solving capabilities beyond the human level. 1,2,3 Computers on the von Neumann architecture are the platform for the breakthroughs albeit frequently powered by hardware accelerators, e.g. graphics processing unit (GPU). 4 The main memory, in this case, is used to store fragmentary information, e.g. weight matrix, representation of hidden neurons, and input datasets, intertwined among the fragments. Therefore, it is conceivable that memory organization is essential to efficient memory retrieval. To this end, memory keeping a weight matrix in place can be considered, in which the matrix matches different representations selectively as a consequence of learning. For instance, a visual input (representation) such as a hand-written '1' recalls a symbolic memory '1' (internal representation) through the stored weight matrix so that the symbolic memory can readily be recalled. In this regard, a high-density crossbar array (CBA) of twoterminal memory elements, e.g. oxide-based resistive memory and phase-change memory, is perhaps a promising solution to machine learning acceleration. 5,6,7,8,9 The connection weight between a pair of neurons is stored in each memory element in the CBA as conductance, and the weight is read out in place by monitoring current in response to a voltage. 5,6,7,8,9 Albeit promising, this approach should address the following challenges; each weight should be pre-calculated beforehand using a conventional error-correcting technique, and the pre-calculated value state needs to be accommodated by a single memory element. The former particularly hinders online learning.In this study, an easy-to-implement algorithm based on a stochastic neural network-termed as the Markov chain Hebbian learning (MCHL) algorithm is proposed. The most notable difference between the MCHL and restricted Boltzmann machine (RBM) 10,11,12,13 is that the MCHL is a discriminative...

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Relaxation oscillator-realized artificial electronic neurons, their responses, and noise

Cited by 43 publications

References 59 publications

Recommended Methods to Study Resistive Switching Devices

Recommended Methods to Study Resistive Switching Devices

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Markov Chain Hebbian Learning Algorithm With Ternary Synaptic Units

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