@inproceedings{4e10da08b427438e9685d763f745459d,
title = "Analog Synaptic Behaviors in Carbon-Based Self-Selective RRAM for In-Memory Supervised Learning",
abstract = "New computational paradigms are required to overcome the von-Neumann bottleneck by reducing main memory access. Neuromorphic and in-memory computing has brought on much promise for improving efficiency in a subset of tasks, and emerging memory technologies are inextricably tied to localized memory accesses. However, the sneak path current (SPC) through unselected neighboring cells is a major challenge occurring in high density storage application in the crossbar array configuration. In this work, carbon-based self-selective memory is shown to overcome the SPC problem and additionally is demonstrated to be a potential candidate as a nanodevice for resource-constrained in-memory supervised learning, by taking advantage of its analog synaptic behaviors. Device variation and non-idealities are characterized in the context of neural network regularization, in fulfilling the aim to reduce the ever-increasing power demands of modern computing.",
keywords = "Memristor, RRAM, Self-selective, Supervised learning, Synaptic",
author = "Chen, {Ying Chen} and Eshraghian, {Jason K.} and Isaiah Shipley and Maxwell Weiss",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 71st IEEE Electronic Components and Technology Conference, ECTC 2021 ; Conference date: 01-06-2021 Through 04-07-2021",
year = "2021",
doi = "10.1109/ECTC32696.2021.00261",
language = "English (US)",
series = "Proceedings - Electronic Components and Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1645--1651",
booktitle = "Proceedings - IEEE 71st Electronic Components and Technology Conference, ECTC 2021",
}