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Low-Voltage Oscillatory Neurons for Memristor-Based Neuromorphic Systems
Qilin Hua1,2, Huaqiang Wu1, Bin Gao1
1Institute of Microelectronics Tsinghua University Beijing 100084 China.
Global Challenges (Hoboken, NJ)
|November 7, 2019
Summary
Researchers developed a novel Ag filamentary threshold switching memristor (TS) for artificial neurons. This low-voltage, low-power device enables efficient leaky integrate-and-fire functions and spiking output for neuromorphic computing.
Area of Science:
- Neuromorphic Engineering
- Materials Science
- Artificial Intelligence
Background:
- Neuromorphic systems mimic the brain for efficient information processing, overcoming von Neumann architecture limitations.
- Existing artificial neurons often suffer from high power consumption due to high operation voltage and leakage current.
- There is a need for energy-efficient artificial neuron designs that emulate biological models.
Purpose of the Study:
- To present a novel oscillatory neuron based on Ag filamentary threshold switching memristor (TS).
- To demonstrate the neuron's ability to perform essential functions like leaky integrate-and-fire and spiking output.
- To evaluate its performance in a memristor-based spiking neural network for image recognition.
Main Methods:
- Fabrication and characterization of an Ag filamentary threshold switching memristor (TS) as an artificial neuron.
- Integration of the TS neuron with a resistive switching memristor (RS) acting as a synapse.
- Testing the self-oscillation behavior and frequency modulation with varying input voltage and synaptic conductance.
- Evaluating the performance of the combined system for image recognition on a CIFAR-10 subset.
Main Results:
- The TS oscillatory neuron operates at a low voltage (<0.6 V) with ultralow power consumption (<1.8 µW) and high endurance (>10^8 cycles).
- The neuron successfully exhibits leaky integrate-and-fire dynamics and threshold-driven spiking output.
- Self-oscillation frequency is proportional to input pulse voltage and synaptic conductance, enabling weighted sum integration.
- The memristor-based spiking neural network achieved 79.2 ± 2.4% accuracy for CIFAR-10 subset image recognition.
Conclusions:
- The Ag filamentary TS memristor offers a promising solution for energy-efficient artificial neurons.
- The combination of TS neuron and RS synapse effectively implements key functions for spiking neural networks.
- This memristor-based approach demonstrates potential for low-power, high-performance neuromorphic computing applications, including image recognition.
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