GreenDL: Green Deep Learning for Edge Devices
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Despite the continuous improvement of deep learning (DL) design and deployment frameworks, an energy-efficient design process guaranteeing user constraints (accuracy, latency, and energy consumption) is still missing from the energy saving perspective.
GreenDL aims to develop theoretical foundations and practical algorithms that (i) enable designing scalable and energy-efficient DL models with low energy footprint and (ii) facilitate fast deployment of complicated DL models for a diverse set of Edge devices satisfying given hardware constraints. To address research challenges, we will design the greenDL framework for energy-efficient design and deployment of DLs on Edge devices.
| First Name | Last Name | Title |
|---|---|---|
| Masoud | Daneshtalab | Professor |
| Mohammad | Loni | Affiliated researcher |
An Efficient Architecture for Edge AI Federated Learning with Homomorphic Encryption (Jun 2025) Dadmehr Rahbari , Masoud Daneshtalab, Maksim Jenihhin Journal of IEEE Access (IEEE-Access)
FORTUNE: A Negative Memory Overhead Hardware-Agnostic Fault TOleRance TechniqUe in DNNs (Dec 2024) Samira Nazari , Mahdi Taheri , Ali Azarpeyvand , Tara Ghasempouri , Masoud Daneshtalab, Maksim Jenihhin 33rd IEEE Asian Test Symposium (ATS-24)
Enhancing Global Model Performance in Federated Learning with Non-IID Data using a Data-Free Generative Diffusion Model (Oct 2024) MohammadReza Najafi , Masoud Daneshtalab, Jeong-A Lee , Seokjoo Shin Journal of IEEE Access (IEEE-Access)
Autonomous Realization of Safety-and Time-Critical Embedded Artificial Intelligence (Mar 2024) Joakim Lindén, Andreas Ermedahl, Hans Salomonsson , Masoud Daneshtalab, Bjorn Forsberg , Paris Carbon Design, Automation & Test in Europe Conference (DATE'24)
A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks (Jan 2024) Mohammad Ahmadilivani , Mahdi Taheri , Jaan Raik , Masoud Daneshtalab, Maksim Jenihhin ACM Computing Surveys (CSUR)
Analysis and Improvement of Resilience for Long Short-Term Memory Neural Networks (Oct 2023) Mohammad Ahmadilivani , Jaan Raik , Masoud Daneshtalab, Alar Kuusik 36th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT 2023)
