The EU is undergoing rapid digitalisation across all sectors. Policy targets aim for an ambitious digital landscape by 2030 with 75% of EU companies using Cloud, AI, and/or Big Data and 10,000 climate-neutral highly secure edge nodes deployed. In order for these targets to be achieved, fostering resilience and industrial leadership in Europe, there are significant steps that need to be taken and sustainability aspects to be considered, including energy and resource optimisation, with no environmental harm and always with the human factor at its core. The growing use of Artificial Intelligence (AI) has already transformed many industries, enabling new capabilities and driving economic growth. However, the energy consumption and data requirements for training AI systems are becoming unsustainable and their environmental impact cannot be ignored, while end-users’ trust appears to be in decline. In fact, there is a growing number of cases where cloud or high-performance computing is required to train high performance AI models, although more and more use cases are demanding the use of AI in a distributed manner at the edge, most often across devices with very different capabilities and limitations.
But, what if it was possible to leverage the benefits of both cloud and edge computing at the same time by optimising the use of resources and AI models? What if it could be executed in a trustworthy way such that hardware, software and network specifications, capabilities and limitations were taken into consideration? Finally, what if we could develop novel algorithms to train lighter AI models and also incorporate highly specialised edge hardware accelerators, in particular neuromorphic chips, within a cloud-edge continuum? We could drastically improve the overall performance of AI applications, reducing carbon footprint, while preserving human values, towards bringing Europe’s digital decade targets one step closer.
The overall objective of MANOLO is to deliver a complete and trustworthy stack of algorithms and tools to help AI practitioners and their systems reach better efficiency and seamless optimization in their operations, resources and data required to train, deploy and run high-quality and lighter AI models in both centralised and cloud-edge distributed environments. To achieve such an endeavour and create impact, MANOLO will:
1- Design next-generation hardware-aware AI algorithms using energy-performance model architecture optimisation via novel approaches in compression, meta-adaptive learning, neural network search and growth
2- Implement a trustworthy framework for i) data management to guarantee traceability, security, and reproducibility of data, models and metadata, and ii) generation of high-quality compressed (meta)data to support the development of novel data-efficient AI algorithms
3- Introduce future-proof trustworthy AI algorithms to evaluate explainability and robustness of models and their efficiency through a holistic end-tο-end benchmarking framework
4- Optimise and automate the allocation of efficient AI models, functions (training and inference) and data in the Cloud-Edge continuum according to requirements and constraints of resources and infrastructures
5- Ensure AI trustworthiness and legal compliance development and operation by-design
6- Demonstrate, evaluate, and validate MANOLO across diverse AI-paradigms and multidimensional use cases under lab stress testing and realistic conditions in relevant environments
7- Establish synergies & collaboration activities while also exchanging knowledge and driving the sustainable exploitation of results in line with the objectives of the AI, Data and Robotics Partnership
