Reliable Hyperdimensional Reasoning on Unreliable Emerging Technologies

Hamza Errahmouni Barkam, Sanggeon Yun, Hanning Chen, Paul Gensler, Albi Mema, Andrew Ding, George Michelogiannakis, Hussam Amrouch, Mohsen Imani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

While Graph Neural Networks (GNNs) have demonstrated remarkable achievements in knowledge graph reasoning, their computational efficiency on conventional computing platforms is impeded by the memory wall problem. To overcome these challenges, we introduce an innovative algorithm-hardware solution that harnesses the potential of hyperdimensional computing (HDC) for robust and memory-centric computation on computing in-memory (CiM) platforms. Departing from traditional graph neural networks, the proposed HDC reasoning model employs a symbolic approach to effectively encode graph entities and their relationships as high-dimensional neural activity. Complementing this approach is a customized Computing-in-Memory (CiM) architecture based on advanced Ferroelectric Field-Effect Transistor (FeFET) technology, which incorporates a precise characterization of non-idealities. This modeling enables the generation of an HDC-tailored model that faithfully represents the hardware architecture. Despite the non-idealities inherent in emerging CiM technologies, our platform demonstrates performance on par with traditional von Neumann architectures for substantial combinations of FeFET device parameters. Our solution overcomes FeFET CiM the increased non-idealities from down-scaled 3nm, operating effectively under all possible configurations when 50 graph edges are considered. Scenarios with less than 4-bit precision per FeFET device cannot handle graphs with more than 200 edges, whereas the 4-bit case can achieve a 90.3% graph reconstruction rate on the worst-case scenario of 80% of noise.

Original languageEnglish
Title of host publication2023 42nd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350315592
DOIs
StatePublished - 2023
Event42nd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2023 - San Francisco, United States
Duration: 28 Oct 20232 Nov 2023

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152

Conference

Conference42nd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2023
Country/TerritoryUnited States
CitySan Francisco
Period28/10/232/11/23

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