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 | Researcher |
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)
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)
APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors (May 2023) Mahdi Taheri , Mohammad Ahmadilivani , Maksim Jenihhin , Masoud Daneshtalab, Jaan Raik International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)
