Move the Roof: Model-driven Methodology for Designing Efficient Deep Learning Architectures
Paper in proceeding, 2026
Designing efficient architectures for Deep Learning (DL) at the edge is challenging, as the growing demands of emerging models contrast with the tight resource constraints. This work proposes a model-driven methodology for the fast exploration of large design spaces, producing efficient architectures under performance constraints. Our methodology combines a strategy to aggressively prune the design space with a Cache-Aware Roofline Model (CARM)-based performance estimation to avoid costly simulations.
runtime
efficiency
edge
design space exploration
deep learning
design methodology
Author
Alexandre Rodrigues
Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento em Lisboa
Mateo Vázquez Maceiras
Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento em Lisboa
Mohammad Ali Maleki
Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento em Lisboa
Muhammad Waqar Azhar
Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)
University of Gothenburg
Aleksandar Ilic
University of Lisbon
Pedro Petersen Moura Trancoso
Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)
University of Gothenburg
Leonel Sousa
ZeroPoint Technologies AB
Proceedings of the ACM Symposium on Applied Computing
664-666
9798400722943 (ISBN)
41st Annual ACM Symposium on Applied Computing, SAC 2026
Thessaloniki, Greece,
Thessaloniki, Greece,
The European Processor Initiative (EPI)
European Commission (EC) (EC/H2020/800928), 2018-12-01 -- 2021-11-30.
European, extendable, energy-efficient, energetic, embedded, extensible, Processor Ecosystem (eProcessor)
European Commission (EC) (EC/H2020/956702), 2021-01-01 -- 2024-06-30.
EPI SGA2
European Commission (EC) (101036168), 2022-01-01 -- 2024-12-31.
Subject Categories (SSIF 2025)
Computer Sciences
DOI
10.1145/3748522.3779938