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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
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Graphene memristive synapses for high precision neuromorphic computing
Thomas F Schranghamer1, Aaryan Oberoi1, Saptarshi Das2,3,4
1Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16802, USA.
Nature Communications
|October 30, 2020
Summary
Graphene memristors offer over 16 programmable states for artificial neural networks, overcoming limitations in current memristive devices. This enables more accurate AI computations and improved on-chip training for neural networks.
Area of Science:
- Materials Science
- Computer Engineering
- Artificial Intelligence
Background:
- Memristive crossbar architectures are key for in-memory computing in artificial neural networks (ANNs).
- Limited non-volatile states in current memristors cause weight rounding errors, reducing ANN inference accuracy and hindering on-chip training.
- This necessitates novel memristive devices with higher precision for advanced AI applications.
Purpose of the Study:
- To introduce graphene-based memristive synapses with a high number of non-volatile, arbitrarily programmable conductance states.
- To address the limitations of conventional memristors in ANNs, specifically concerning weight quantization errors and training efficiency.
- To demonstrate the potential of these advanced memristors for improved AI hardware.
Main Methods:
- Fabrication and characterization of graphene-based memristive devices.
- Demonstration of multi-level (>16) and non-volatile conductance states with high programming endurance and retention.
- Implementation of vector-matrix multiplication using these memristors with k-means clustering for weight assignment.
Main Results:
- Graphene memristors exhibit >16 programmable non-volatile conductance states with excellent retention and endurance.
- K-means clustering-based weight assignment with graphene memristors significantly improves computing accuracy compared to uniform quantization.
- The developed memristors effectively enable precise weight representation crucial for efficient ANN operation.
Conclusions:
- Graphene-based memristive synapses overcome the state limitations of conventional memristors for ANNs.
- Arbitrarily programmable conductance states enhance accuracy and enable efficient on-chip training in AI hardware.
- These advanced memristors represent a significant step towards more powerful and accurate in-memory computing systems.
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