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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Published on: March 9, 2019

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

This summary is machine-generated.

Researchers developed a reconfigurable memristive field-programmable analog array (memFPAA) for advanced analog computing. This platform enables efficient signal processing and neuromorphic computing applications.

Keywords:

FPAA VMM analog computing audio equalizer memristor

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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.