Binarized Neural Network Comprising Quasi‐Nonvolatile Memory Devices for Neuromorphic Computing

This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing (≤ 5 mV dec−1) and a high on/off ratio (≥ 107). A pair of QNVM devices are used for a single syna...

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Bibliographic Details
Published in:Advanced electronic materials Vol. 10; no. 9
Main Authors: Shin, Yunwoo, Jeon, Juhee, Cho, Kyoungah, Kim, Sangsig
Format: Journal Article
Language:English
Published: Seoul John Wiley & Sons, Inc 01.09.2024
Wiley-VCH
Subjects:
Accuracy
Arrays
binarized neural network
Energy consumption
Feedback loops
image recognition
Memory devices
multiply‐accumulate
Neural networks
Neuromorphic computing
Positive feedback
positive feedback loop
quasi‐nonvolatile memory
Silicon
ISSN:2199-160X, 2199-160X
Online Access:Get full text
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Summary:This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing (≤ 5 mV dec−1) and a high on/off ratio (≥ 107). A pair of QNVM devices are used for a single synaptic cell in a cell array, in which its memory state represents the synaptic weight, and the voltages applied to the pair act as input in a complementary fashion. The array of synaptic cells performs matrix multiply‐accumulate (MAC) operations between the weight matrix and input vector using XNOR and current summation. All the results of the MAC operations and vector‐matrix multiplications are equivalent. Moreover, the BNN features a high accuracy of 93.32% in the MNIST image recognition simulation owing to high device uniformity (1.35%), which demonstrates the feasibility of compact and high‐performance neuromorphic computing. In this study, quasi‐nonvolatile memory devices with the p+‐n‐p‐n+ structure are proposed as synaptic devices. The 2×2 synaptic cell array demonstrates the matrix multiply‐accumulate operations using XNOR and current summations. Furthermore, binarized neural network achieves high accuracy (93.32%) in MNIST image recognition simulation. Consequently, the devices provide synaptic cell array for high‐performance compact neuromorphic computing architecture.
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ISSN:2199-160X
2199-160X
DOI:10.1002/aelm.202400061