Compact Modeling and Mitigation of Parasitics in Crosspoint Accelerators of Neural Networks

In-memory computing (IMC) can accelerate data-intensive tasks, such as matrix-vector multiplication (MVM) or artificial neural networks (ANNs) inference, by means of the crosspoint memory array, allowing to reduce time and energy consumption. IMC accuracy, however, is affected by nonidealities, such...

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Bibliographic Details
Published in:	IEEE transactions on electron devices Vol. 71; no. 3; pp. 1 - 7
Main Authors:	Lepri, N., Glukhov, A., Mannocci, P., Porzani, M., Ielmini, D.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Arrays Artificial neural networks Deep learning emerging memory technologies Energy consumption hardware accelerator in-memory computing (IMC) Logic gates Mathematical analysis Matrix algebra Microprocessors Programming Resistance resistive switching memory (RRAM) Transistors Voltage Wires
ISSN:	0018-9383, 1557-9646
Online Access:	Get full text
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