A New High Speed Low Power Dissipation Three-Element Si-Based SRAM Cell

Tong, Xiaodong; Wu, Hao; Zhao, Lingjuan; Zhong, Huicai

doi:10.1149/05201.0105ecst

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…A key component of this effect is a threshold switch that represents monostable resistive switching accompanied by the S‐shape NDR effect . Threshold switching behavior has been observed in a wide range of materials systems, such as higher chalcogenides, Mott insulators, and Shockley diodes . Recently, Pickett et al proposed an LIF neuron model using a pair of Pearson‐Anson oscillators referred to as a “neuristor.” Each oscillator includes an NbO 2 ‐based Mott insulator sandwiched between inert Pt electrodes that exhibits threshold switching with respect to an applied voltage, which in turn leads to a change in lattice temperature crossing the critical temperature.…”

Section: Neuromorphic Computingmentioning

confidence: 99%

Memristors for Energy‐Efficient New Computing Paradigms

Jeong

Kim

et al. 2016

Adv Elect Materials

330

203

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In this Review, memristors are examined from the frameworks of both von Neumann and neuromorphic computing architectures. For the former, a new logic computational process based on the material implication is discussed. It consists of several memristors which play roles of combined logic processor and memory, called stateful logic circuit. In this circuit confi guration, the logic process fl ows primarily along a time dimension, whereas in current von Neumann computers it occurs along a spatial dimension. In the stateful logic computation scheme, the energy required for the data transfer between the logic and memory chips can be saved. The non-volatile memory in this circuit also saves the energy required for the data refresh. Neuromorphic (cognitive) computing refers to a computing paradigm that mimics the human brain. Currently, the neuromorphic or cognitive computing mainly relies on the software emulation of several brain functionalities, such as image and voice recognition utilizing the recently highlighted deep learning algorithm. However, the human brain typically consumes ≈10-20 Watts for selected "human-like" tasks, which can be currently mimicked by a supercomputer with power consumption of several tens of kilo-to megawatts. Therefore, hardware implementation of such brain functionality must be eventually sought for power-effi cient computation. Several fundamental ideas for utilizing the memristors and their recent progresses in these regards are reviewed. Finally, material and processing issues are dealt with, which is followed by the conclusion and outlook of the fi eld. These technical improvements will substantially decrease the energy consumption for futuristic information technology.

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Section: Neuromorphic Computingmentioning

confidence: 99%

Memristors for Energy‐Efficient New Computing Paradigms

Jeong

Kim

et al. 2016

Adv Elect Materials

330

203

View full text Add to dashboard Cite

show abstract

“…The S-shaped NDR engaged in threshold switching has been observed in not only amorphous higher chalcogenides [23][24][25] but also various systems such as Mott insulators, 18,26 open-base bipolar junctions, 27 and Shockley diodes. 28,29 The amorphous GeSe in the TS is an As-free chalcogenide material whose details in film deposition can be seen elsewhere. 24 The S-shaped NDR causing the on-state drives a large current flow, and thus high power dissipation (Joule heating).…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2016

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A proof-of-concept relaxation oscillator-based leaky integrate-and-fire (ROLIF) neuron circuit is realized by using an amorphous chalcogenide-based threshold switch and non-ideal operational amplifier (op-amp). The proposed ROLIF neuron offers biologically plausible features such as analog-type encoding, signal amplification, unidirectional synaptic transmission, and Poisson noise. The synaptic transmission between pre- and postsynaptic neurons is achieved through a passive synapse (simple resistor). The synaptic resistor coupled to the non-ideal op-amp realizes excitatory postsynaptic potential (EPSP) evolution that evokes postsynaptic neuron spiking. In an attempt to generalize our proposed model, we theoretically examine ROLIF neuron circuits adopting different non-ideal op-amps having different gains and slew rates. The simulation results indicate the importance of gain in postsynaptic neuron spiking, irrespective of the slew rate (as long as the rate exceeds a particular value), providing the basis for the ROLIF neuron circuit design. Eventually, the behavior of a postsynaptic neuron in connection to multiple presynaptic neurons via synapses is highlighted in terms of EPSP evolution amid simultaneously incident asynchronous presynaptic spikes, which in fact reveals an important role of the random noise in spatial integration.

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A New High Speed Low Power Dissipation Three-Element Si-Based SRAM Cell

Cited by 2 publications

References 7 publications

Memristors for Energy‐Efficient New Computing Paradigms

Memristors for Energy‐Efficient New Computing Paradigms

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

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