z-logo
open-access-imgOpen Access
Algebraic Expressions for Complex Deep Learning Systems Modeling and Analysis
Author(s) -
Rhandall Valdez-Yepez,
Bryan V. Piguave,
Jennifer C. Pita,
Andres G. Abad
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3595486
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Many modern Deep Learning (DL) systems have achieved impressive state-of-the-art results by combining individual sub-systems, including foundation models, to form increasingly more complex DL systems. However, this growing complexity also introduces new challenges in effectively modeling and analyzing these systems. In this paper, we introduce a novel approach to represent complex DL systems using algebraic expressions, by decomposing them into sets of sub-systems arranged in parallel and series configurations, allowing for abstract and efficient representations. We further show how these algebraic expressions can be readily used to compute specific performance metrics of DL systems by formulating specific operations on our algebraic expressions. Specifically, we present the operations for computing system inference time and accuracy. Finally, we present two practical real case studies addressing multi-class visual classification of retail products, demonstrating the effectiveness and advantages of using our methodology in modeling and analyzing these systems.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom