Eco-conception

Overall SaaS spending fell year over year, but shadow IT overspend and app redundancies persisted, Zylo research found.

Si les problèmes publics qui entourent le développement de l’Intelligence Artificielle (IA) font souvent les gros titres (discriminations raciales, surveillance de masse, automatisation des emplois, etc), la conjonction du mouvement critique contre la tech et des activistes du climat pointe, depuis quelques années, de manière plus discrète, les effets néfastes de l’IA sur l'environnement.

In this post, I want to continue building out an incremental model, but rather than focusing on emissions calculations I want to create a model to estimate energy use.

More than 272 million new laptops are manufactured every year, making the IT industry responsible for as much greenhouse gas pollution as the entire airline industry. This equates to the IT industry contributing 2% of global carbon dioxide (CO2) emissions.  Internet usage continues to rise, with more people using mobiles and tablets in their everyday … What Is The Carbon Footprint Of A Laptop? Read More »

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Generative AI (Gen-AI) represents a major growth potential for the digital industry, a new stage in digital transformation through its many applications. Unfortunately, by accelerating the growth of digital technology, Gen-AI is contributing to the significant and multiple environmental damage caused by its sector. The question of the sustainability of IT must include this new technology and its applications, by measuring its environmental impact. To best respond to this challenge, we propose various ways of improving the measurement of Gen-AI's environmental impact. Whether using life-cycle analysis methods or direct measurement experiments, we illustrate our methods by studying Stable Diffusion a Gen-AI image generation available as a service. By calculating the full environmental costs of this Gen-AI service from end to end, we broaden our view of the impact of these technologies. We show that Gen-AI, as a service, generates an impact through the use of numerous user terminals and networks. We also show that decarbonizing the sources of electricity for these services will not be enough to solve the problem of their sustainability, due to their consumption of energy and rare metals. This consumption will inevitably raise the question of feasibility in a world of finite resources. We therefore propose our methodology as a means of measuring the impact of Gen-AI in advance. Such estimates will provide valuable data for discussing the sustainability or otherwise of Gen-AI solutions in a more transparent and comprehensive way. We intend to help this discussion by differentiating in our approach between the embodied and operational impacts of Gen-AI. In this way, we can consider the sustainability of models, as we already do for equipment.

Using Umbraco Delivery API, NextJS's Static Site Generation, and uSync, to create an ultra-low energy Umbraco website

DIMPACT

Sustainability is now a key focus for the digital advertising industry. Last year, it was estimated that a typical ad campaign emitted around 5.4 tons of CO2, with a programmatic ad impression producing around one gram of Co2 emissions. When the number of ad impressions transacted on a regular basis…

We're excited to announce a proposed standard for assessing technology-related carbon emissions – an approach to classifying an organisation's technology footprint in a way that enables consistent analysis and benchmarking of the carbon impact, aligned with the GHG protocol.

Towards environmentally sustainable computational science

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L'intelligence collective est un levier pour déployer la démarche d'écoconception numérique dans une organisation : on vous explique pourquoi

L’écoconception numérique continue à se développer en France, notamment portée par le nouveau réglement général d’écoconception de services numériques (RGESN) développé au sein de l’État. Cette pratique consiste à se demander quoi faire pour réduire la charge environnementale (émissions de gaz à effet de serre, consommation d’énergie, d’eau, etc.) d’un site web et de son usage. Nous avons maintenant une certaine expérience du sujet puisque nous explorons à la fois en théorie et en pratique depuis 5 ans toutes ces questions. La refonte de Telecoop s’inscrit d’ailleurs dans la continuité de celle pour Commown avec Derek Salmon. Dans cette étude de cas, nous allons tâcher d’appliquer ce fameux RGESN qui se distingue par 8 grands axes : la stratégie, les spécifications, l’architecture, l’UX/UI, les contenus, le front-end, le back-end, l’hébergement. Nous allons reprendre point par point chacun de ces éléments.

Découvrez la conférence de Christophe Clouzeau, qui présente l'importance d'une écoconception et de l'impact environnemental du numérique.

Achieving the UN Sustainable Development Goals (SDGs) demands a shift by industry, governments, society, and individuals to reach adequate levels of a…

Smart coders can see the system is broken

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.

Pistes d'interfaces pour un design écologique des paramètres sur mobile.