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Updated: Aug 21, 2025
08:07
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
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Memristive Field-Programmable Analog Arrays for Analog Computing
Yunning Li1, Wenhao Song2, Zhongrui Wang1
1Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA.
Advanced Materials (Deerfield Beach, Fla.)
|November 15, 2022
Summary
Researchers developed a reconfigurable memristive field-programmable analog array (memFPAA) for advanced analog computing. This platform enables efficient signal processing and neuromorphic computing applications.
Area of Science:
- Electronics
- Computer Engineering
- Materials Science
Background:
- Analog computing is gaining interest due to its potential for handling large analog data volumes.
- Memristors are novel electronic elements enabling advancements in analog computing.
- Reconfigurable platforms are needed for multipurpose analog computing applications.
Purpose of the Study:
- To experimentally demonstrate a memristive field-programmable analog array (memFPAA).
- To showcase the memFPAA's reconfigurability for various analog functions.
- To highlight the potential of memFPAA in signal processing and neuromorphic computing.
Main Methods:
- Developed a memFPAA platform integrating memristive devices and CMOS components.
- Configured memristive devices as core analog elements and routing networks.
- Utilized memristive vector-matrix multipliers for computational tasks.
Main Results:
- Successfully demonstrated the memFPAA's reconfigurability.
- Implemented a first-order band pass filter, an audio equalizer, and an acoustic mixed frequency classifier.
- Showcased the integration of programmable analog memristors and memristive routing networks.
Conclusions:
- The memFPAA offers a versatile platform for analog computing.
- This technology facilitates rapid prototyping of analog designs.
- The memFPAA presents opportunities for efficient analog applications in signal processing and neuromorphic computing.
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