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. 

[Show all publications]

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)

Efficient On-device Transfer Learning using Activation Memory Reduction (Sep 2023)
Amin Yoosefi , Seyedhamidreza Mousavi, Masoud Daneshtalab, Mehdi Kargahi
International Conference on Fog and Mobile Edge Computing (FMEC)

Enhancing Fault Resilience of QNNs by Selective Neuron Splitting (Jun 2023)
Mohammad Ahmadilivani , Javid Taheri , Jaan Raik , Maksim Jenihhin , Masoud Daneshtalab
5th IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) (AICAS 2023)

NeuroPIM: Flexible Neural Accelerator for Processing-in-Memory Architectures (May 2023)
Ali Monavari , Sepideh Fattahi , Mehdi Modarressi , Masoud Daneshtalab
International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)

Masoud Daneshtalab, Professor

Phone: +4621103111