Eco-conception

Learn about optimizing your Azure carbon emissions.

Deep Learning (DL) frameworks such as PyTorch and TensorFlow include runtime infrastructures responsible for executing trained models on target hardware, managing memory, data transfers, and multi-accelerator execution, if applicable. Additionally, it is a common practice to deploy pre-trained models on environments distinct from their native development settings. This led to the introduction of interchange formats such as ONNX, which includes its runtime infrastructure, and ONNX Runtime, which work as standard formats that can be used across diverse DL frameworks and languages. Even though these runtime infrastructures have a great impact on inference performance, no previous paper has investigated their energy efficiency. In this study, we monitor the energy consumption and inference time in the runtime infrastructures of three well-known DL frameworks as well as ONNX, using three various DL models. To have nuance in our investigation, we also examine the impact of using different execution providers. We find out that the performance and energy efficiency of DL are difficult to predict. One framework, MXNet, outperforms both PyTorch and TensorFlow for the computer vision models using batch size 1, due to efficient GPU usage and thus low CPU usage. However, batch size 64 makes PyTorch and MXNet practically indistinguishable, while TensorFlow is outperformed consistently. For BERT, PyTorch exhibits the best performance. Converting the models to ONNX usually yields significant performance improvements but the ONNX converted ResNet model with batch size 64 consumes approximately 10% more energy and time than the original PyTorch model.

Alter Numeris, ce sont des chercheurs et des penseurs qui réfléchissent les enjeux de la société numérique.

If you want to save money in Enterprise IT, it turns out that sustainability as a KPI is more important than cost - this is one of the key findings coming from the recent GreenOps Survey, some research made possible by the recent ClimateAction MiniGrants fund.

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Als ervaren digitale experts helpen we organisaties met toegankelijke technologie. Strategie, UX, websites, technologie, online marketing...

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Request PDF | On Jan 1, 2014, Olivier Philippot and others published Characterization of the energy consumption of websites: Impact of website implementation on resource consumption | Find, read and cite all the research you need on ResearchGate

Request PDF | On Sep 1, 2016, Marc Vautier and others published Is “software eco-design” a solution to reduce the environmental impact of electronic equipments? | Find, read and cite all the research you need on ResearchGate

This contributed volume provides illuminating perspectives on reducing power consumption in IP-based video distribution networks (VDNs).

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Designers Éthiques est une association qui explore les pratiques de conception numérique. Nous travaillons à la capacitation des designers et des professionnels du numérique pour produire un numérique émancipateur, durable et désirable.

From a Earthshot Prize Nominee, World Green Technology Leader, Digital Ambassador of the Year and Google Brain Award Winner Decarbonise Digital | Sustainable IT | Climate Change Solutions

Strategies and Best Practices for Reducing Environmental Harms in a Digital World

Innovations in Systems and Software Engineering - Placing sustainability at the core of computing practices, the industry is poised to pioneer positive changes and create a cleaner and more...

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