ALOHA: an architectural-aware framework for deep learning at the edge. - Paolo Meloni; Daniela Loi; Gianfranco Deriu; Andy D. Pimentel; Dolly Sapra; Bernhard Moser 001; Natalia Shepeleva; Francesco Conti 001; Luca Benini; Oscar Ripolles; David Solans; Maura Pintor; et al. (INTESA@ESWEEK, 2018)

Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project. - Paolo Meloni; Daniela Loi; Gianfranco Deriu; Andy D. Pimentel; Dolly Sapra; Maura Pintor; Battista Biggio; Oscar Ripolles; David Solans; Francesco Conti 001; Luca Benini; Todor P. Stefanov; et al. (ICM, 2018)

Optimization and deployment of CNNs at the edge: the ALOHA experience. - Paolo Meloni; Daniela Loi; Paola Busia; Gianfranco Deriu; Andy D. Pimentel; Dolly Sapra; Todor P. Stefanov; Svetlana Minakova; Francesco Conti 001; Luca Benini; Maura Pintor; Battista Biggio; et al. (CF, 2019)

Buffer Sizes Reduction for Memory-efficient CNN Inference on Mobile and Embedded Devices. - Svetlana Minakova; Todor P. Stefanov (DSD, 2020)

Combining Task- and Data-Level Parallelism for High-Throughput CNN Inference on Embedded CPUs-GPUs MPSoCs. - Svetlana Minakova; Erqian Tang; Todor P. Stefanov (SAMOS, 2020)

Energy-Efficient and High-Throughput CNN Inference on Embedded CPUs-GPUs MPSoCs. - Erqian Tang; Svetlana Minakova; Todor P. Stefanov (SAMOS, 2021)

ALOHA: A Unified Platform-Aware Evaluation Method for CNNs Execution on Heterogeneous Systems at the Edge. - Paola Busia; Svetlana Minakova; Todor P. Stefanov; Luigi Raffo; Paolo Meloni (IEEE Access, 2021)

Scenario Based Run-Time Switching for Adaptive CNN-Based Applications at the Edge. - Svetlana Minakova; Dolly Sapra; Todor P. Stefanov; Andy D. Pimentel (ACM Trans. Embed. Comput. Syst., 2022)

Memory-Throughput Trade-off for CNN-Based Applications at the Edge. - Svetlana Minakova; Todor P. Stefanov (ACM Trans. Design Autom. Electr. Syst., 2023)