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 |
SRCPAR - Spike Response based Congestion Prediction for Adaptive Routing for 2D NoCs (Jun 2025) RAJENDRA SINGH , MANOJ BOHRA , Ashish Sharma , SOURABH SINGH VERMA , SOURABH SINGH VERMA , AMIT KUMAR BAIRWA , Masoud Daneshtalab Journal of IEEE Access (IEEE-Access)
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)
AdAM: Adaptive Approximate Multiplier for Fault Tolerance in DNN Accelerators (Feb 2025) Mahdi Taheri , Natalia Cherezova , Samira Nazari , Ali Azarpeyvand , Tara Ghasempouri , Masoud Daneshtalab, Jaan Raik , Maksim Jenihhin IEEE Transactions on Device and Materials Reliability (TDMR)
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)
