Advances in Memristor-Based Neural Networks

Xu, Weilin; Wang, Jingjuan; Yan, Xiaobing

doi:10.3389/fnano.2021.645995

Cited by 81 publications

(41 citation statements)

References 112 publications

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“…[199] Memristive devices with synaptic plasticity provide the condition for implementing bio-plausible learning rules in SNNs. [200,201] The memristors' switching behavior and relaxation dynamic are utilized to implement the neuronal function of threshold logic. For the same neuron model, it simply uses its weight to calculate forward in ANNs, while in SNNs, it only switches to the working mode when the nerve spike received exceeds its spike threshold, then an output pulse will be sent out.…”

Section: Comparisons Between Memristor-based Anns and Snnsmentioning

confidence: 99%

Memristor‐Based Intelligent Human‐Like Neural Computing

Wang

Song

Chen

et al. 2022

Adv Elect Materials

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In the field of a relatively dangerous working environment, NASA developed Valkyrie, a 44-degree-of-freedom, series elastic actuator-based robot. In addition, Valkyrie is designed to respond to disasters like nuclear disasters and advance human spaceflight in extraterrestrial planetary settings. [6] By implementing safety features and allowing remote intervention, Atkeson et al. enabled an Atlas humanoid robot to meet the standard in performing disaster response-related tasks involving physical contact with the environment. [7] Currently, to allow humanoid robots to feel and process the environmental information as human beings for perfectly implementing tasks, perceptual comprehension and computation efficiency are two key indexes. Nevertheless, it is challenging to achieve them. The former requires the installation of massive, diverse sensors in humanoids, which will inevitably slow down the processing speed under the current sensing-storage-process separated framework, i.e., von Neumann architecture. In other words, the current computer architecture builds up a barrier between sensing performance and computation efficiency. In this context, neuromorphic devices which can emulate the perceptual and computation functions of the biological nervous system have illustrated their potential to break the von Neumann barrier, attracting researchers' interests (Figure 1). Humanoid robots, intelligent machines resembling the human body in shape and functions, cannot only replace humans to complete services and dangerous tasks but also deepen the own understanding of the human body in the mimicking process. Nowadays, attaching a large number of sensors to obtain more sensory information and efficient computation is the development trend for humanoid robots. Nevertheless, due to the constraints of von Neumann-based structures, humanoid robots are facing multiple challenges, including tremendous energy consumption, latency bottlenecks, and the lack of bionic properties. Memristors, featured with high similarity to the biological elements, play an important role in mimicking the biological nervous system. The memristor-based nervous system allows humanoid robots to obtain high energy efficiency and bionic sensing properties, which are similar properties to the biological nervous system. Herein, this article first reviews the biological nervous system and memristor-based nervous system thoroughly, including the structures and also the functions. The applications of memristor-based nervous systems are introduced, the difficulties that need to be overcome are put forward, and future development prospects are also discussed. This review can hopefully provide an evolutionary perspective on humanoid robots and memristor-based nervous systems.

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Section: Comparisons Between Memristor-based Anns and Snnsmentioning

confidence: 99%

Memristor‐Based Intelligent Human‐Like Neural Computing

Wang

Song

Chen

et al. 2022

Adv Elect Materials

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“…To put it another way, each processing work is broken down into a series of MAGIC NOR operations that are carried out one after the other, employing memory cells as computation elements. Memristor Ratioed Logic (MRL) [26] is the third design style of memristor-based logic. The programmable resistance of memristors is used in the computation of the Boolean AND and OR operations in this typical hybrid CMOS-memristor logic architecture.…”

Section: ░ 2 Memristormentioning

confidence: 99%

Performance Evaluation of Low Power Hybrid Combinational Circuits using Memristor

Garg

Wairya²

2022

IJEER

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Recently, extending the use of memristor technology from memory to computing has received a lot of attention. Memristor-based logic design is a new concept that aims to make computing systems more efficient. Several logic families have emerged, each with its own set of characteristics. In this paper, CMOS-based hybrid memristor-based combinational circuits are designed. Many computational devices require combinational circuits. All of the proposed designs were analysed for power, latency, and transistor count. Cadence Virtuoso is used for simulation of circuits. In this study, we used the VTEAM model to describe the simulated memristor because it is easy to understand and gives accurate results.

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“…5 Non-volatile resistive switching is characterized by the permanently stored resistance state after stimulation and has been widely used in constructing feedforward neural networks (FNNs) for static (non-temporal) data processing. 9,10 Volatile resistive switching, on the other hand, features temporally stored resistance states that can represent temporal information of the input signals. Such switching dynamics can therefore be used to directly process temporal data in a biofaithful fashion in recurrent neural networks (RNNs).…”

Section: Introductionmentioning

confidence: 99%