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Towards scalable memristive hardware for spiking neural networks
Peng Chen1,2, Bihua Zhang1, Enhui He1,2
1State Key Laboratory of Brain Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China. penglin@zju.edu.cn.
Materials Horizons
|February 6, 2025
Summary
Spiking neural networks (SNNs) offer efficient AI computation. This review explores memristor devices for scalable SNN hardware, addressing device integration and algorithms.
Area of Science:
- Neuroscience and Artificial Intelligence
- Materials Science and Engineering
Background:
- Spiking neural networks (SNNs) mimic brain dynamics for energy-efficient AI.
- Current hardware struggles to efficiently emulate SNNs.
- Memristor devices show promise for AI acceleration.
Purpose of the Study:
- To review the potential and challenges of memristor devices for SNN implementation.
- To focus on scaling and integration of neuronal and synaptic memristor devices.
- To provide a system-level perspective on memristor-based SNN platforms.
Main Methods:
- Survey of recent progress in memristor device and circuit development for SNNs.
- Discussion of pathways for chip-level integration of memristive components.
- Exploration of hardware-oriented algorithm designs for SNNs.
Main Results:
- Memristor-based systems offer significant potential for SNN acceleration.
- Challenges remain in scaling and integrating neuronal and synaptic memristor devices.
- Progress in device fabrication and circuit design is paving the way for SNN hardware.
Conclusions:
- Memristor technology is a key enabler for scalable and efficient SNN platforms.
- Further research in device integration and hardware-aware algorithms is crucial.
- A system-level approach is necessary for realizing the full potential of memristor-based SNNs.
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