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Transcranial Direct Current Stimulation tDCS for Memory Enhancement

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Published on: September 18, 2021

Retention-aware zero-shifting technique for Tiki-Taka algorithm-based analog deep learning accelerator.

Kyungmi Noh¹, Hyunjeong Kwak¹, Jeonghoon Son¹

¹Department of Materials Science and Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.

Science Advances

|June 14, 2024

Summary

This summary is machine-generated.

We developed 4 K-scale electrochemical random-access memory (ECRAM) arrays for analog neural network training. A new technique optimizes training accuracy despite device retention limitations.

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Area of Science:

Materials Science
Computer Engineering
Artificial Intelligence

Background:

Electrochemical random-access memory (ECRAM) offers potential for analog neural network accelerators.
Non-ideal characteristics of analog memory devices pose challenges for efficient neural network training.

Purpose of the Study:

To fabricate and characterize large-scale ECRAM cross-point arrays for analog neural network training.
To investigate the impact of ECRAM retention characteristics on neural network training performance.
To develop a technique to enhance training accuracy in ECRAM-based systems.

Main Methods:

Fabrication of 4 K-scale ECRAM cross-point arrays.
Electrical characterization of an 8x8 ECRAM array, assessing yield and variations.
Experimental study using ECRAM devices with the Tiki-Taka version 2 (TTv2) algorithm.
Development and application of a retention-aware zero-shifting technique.

Main Results:

Achieved 100% yield for an 8x8 ECRAM array with excellent switching characteristics and low variations.
Demonstrated ECRAM array efficacy in neural network training using TTv2.
Identified retention characteristics as critical factors influencing training accuracy and available weight range.
Showcased improved training performance using the proposed retention-aware zero-shifting technique.

Conclusions:

Large-scale ECRAM arrays are viable for analog neural network training accelerators.
Device retention characteristics significantly impact neural network training outcomes.
The retention-aware zero-shifting technique effectively optimizes training performance for ECRAM devices with limited retention.