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Reliability Analysis of Memristor Crossbar Routers: Collisions and On/off Ratio Requirement

Chen, Junren; Wu, Chenxi; Indiveri, Giacomo; Payvand, Melika (2022). Reliability Analysis of Memristor Crossbar Routers: Collisions and On/off Ratio Requirement. In: 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Glasgow, United Kingdom, 24 October 2022 - 26 October 2022, 9970901.

Abstract

Memristors are commonly used in crossbar arrays as “in-memory computing” elements to solve the von-Neumann bottleneck problem. However, they can also be used as “in-memory routing” elements to configure on-chip interconnection schemes and route signals among computing elements in configurable multi-core neuromorphic processors. While there has been a significant focus on the use of memristive devices as in-memory computing elements, to date, studies on the fundamental reliability properties of memristors as routing elements are still missing. In this paper, we analyze the reliability issues of using these devices in routing crossbar arrays, caused by sharing routing resources (collisions), and undesired pulses due to the leakage paths (on/off ratio requirement). We show that there is a trade-off between routing collision probability and the degree of connectivity (i.e., fan-in) of the receivers sharing routing channels. We provide specifications and guidelines based on a theoretical analysis for engineering the properties of memristive devices, and for designing routing systems based on memristor crossbars.

Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Physical Sciences > Computer Science Applications
Physical Sciences > Computer Vision and Pattern Recognition
Physical Sciences > Hardware and Architecture
Physical Sciences > Electrical and Electronic Engineering
Physical Sciences > Instrumentation
Language:English
Event End Date:26 October 2022
Deposited On:26 Feb 2023 10:19
Last Modified:27 Feb 2023 21:00
OA Status:Green
Publisher DOI:https://doi.org/10.1109/icecs202256217.2022.9970901
Project Information:
  • Funder: H2020
  • Grant ID: 876925
  • Project Title: ANDANTE - Ai for New Devices And Technologies at the Edge
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