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A low energy oxide-based electronic synaptic device for neuromorphic visual systems with tolerance to device
Shimeng Yu1, Bin Gao, Zheng Fang
1Department of Electrical Engineering and Center for Integrated Systems, Stanford University, Stanford, CA 94305, USA. simonyu@stanford.edu
Advanced Materials (Deerfield Beach, Fla.)
|January 29, 2013
Summary
Neuromorphic computing utilizes oxide-based synaptic devices for efficient, low-energy artificial intelligence. These devices demonstrate resilience to variations, crucial for advanced artificial visual systems.
Area of Science:
- * Materials Science and Engineering
- * Computer Science
- * Neuroscience
Background:
- * Conventional von Neumann computing faces limitations in energy efficiency and parallel processing for complex tasks.
- * Neuromorphic computing offers a paradigm shift, mimicking brain structures for enhanced computation.
- * Oxide-based resistive switching memory presents a promising avenue for emulating biological synapses.
Purpose of the Study:
- * To engineer oxide-based resistive switching memory devices for synaptic emulation.
- * To characterize device-level performance, focusing on energy efficiency and resistance modulation.
- * To develop a stochastic model for quantifying device dynamics and variations.
- * To simulate and evaluate the performance of a large-scale artificial visual system using these synaptic devices.
Main Methods:
- * Engineered oxide-based resistive switching memory devices.
- * Characterized gradual resistance modulation using hundreds of identical pulses.
- * Developed a stochastic compact model to analyze device switching dynamics and variations.
- * Simulated an artificial visual system with 16,348 oxide-based synaptic devices for image processing tasks.
Main Results:
- * Achieved low energy consumption of less than 1 pJ per spike at the device level.
- * Quantified device switching dynamics and variations using a developed stochastic compact model.
- * Successfully simulated an artificial visual system demonstrating tolerance to device variation.
- * Validated the effectiveness of oxide-based synaptic devices in image orientation and edge detection tasks.
Conclusions:
- * Oxide-based resistive switching memory devices show significant potential for efficient neuromorphic computing.
- * The developed stochastic model aids in understanding and managing device variations.
- * The simulated artificial visual system highlights the inherent robustness of neuromorphic architectures.
- * This work advances the development of practical, large-scale neuromorphic systems with improved energy efficiency and fault tolerance.
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