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- W4285223846 abstract "Neuromorphic computing has gained a considerable research interest due to its potential in realizing highly efficient parallel computations. However, the existing neuromorphic architectures face various drawbacks. In this study, we present an integrate-and-fire (I&F) neuron using a Li-based electrochemical random access memory (Li-ECRAM) to achieve exceptional area efficiency and low-power neuromorphic computing. The proposed Li-ECRAM neuron employs a significantly reduced number of transistors when compared to other novel nonvolatile memory-based I&F neurons due to linear current integration characteristics and a high linear conductance response to the input current. As the integration-type Li-ECRAM is linear, it eliminates the requirement of a nonlinear compensating circuit. Therefore, a Li-ECRAM-based neuron has a simple structure comprising Li-ECRAM, reset transistor, inverter, and pulse generator. Furthermore, we also evaluate the operation speed and energy consumption of the proposed neuron, demonstrating the potential for high-speed and low-power operation. The proposed neuron can be applied in large-scale neuromorphic hardware applications due to the scalability and low energy consumption of Li-ECRAM." @default.
- W4285223846 created "2022-07-14" @default.
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- W4285223846 date "2022-09-01" @default.
- W4285223846 modified "2023-09-26" @default.
- W4285223846 title "Integrate-and-Fire Neuron With Li-Based Electrochemical Random Access Memory Using Native Linear Current Integration Characteristics" @default.
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- W4285223846 doi "https://doi.org/10.1109/ted.2022.3188241" @default.
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