A Collaborative Research Cloud Infrastructure powering Discovery and Exchange

Collaborative breakthrough research in Artificial Intelligence (AI) requires access to well-tuned systems, specific computing, large datasets, and dedicated storage, often only accessible to a select few. This reality slows down complementary research, as a substantial amount of project time is often dedicated to repeating setups, understanding computation and storage intricacies, data properties, and how to properly transfer systems between institutions, to pursue overall objectives. Examples can be often found in cases encompassing consortia with computational versus non-computational specialists, or containing a virtuous mixture of domain experts (medical doctors, biologists, psychologists) and data scientists. Reproducibility and technology transfer are essential tools in thriving projects, however these concepts are costly to implement and maintain. Deploying AI solutions requires multidisciplinary expertise, the right hardware, and AI specialists to tune and ready tools for collaborative use. In practice, lack of specific expertise slowdowns partner-to-partner communication affecting overall productivity. In projects with industry or government-academia partnerships driven by concrete societal needs, replicating project conclusions with different, private datasets, or allowing partners to infer from pre-trained models using adequate hardware setups, is often avoided because of these barriers. Moreover, the growing scale, complexity and impact of contemporary AI systems such as Large Language Models (LLMs) accelerates the need for accessible infrastructures which can guarantee systematised, transparent and increased collaborative work. We intend to bridge these gaps by building “CollabCloud", a cloud-based research infrastructure to boost collaborative research for current and future projects at the Idiap Research Institute. The main focal points will be boosting the ability of easily exporting researchworkflows, exploring AI models by both computational and non-computational experts, and allowing controlled access to shared storage and computing power for collaborative projects. Beyond these goals, CollabCloud will enable Idiap to participate in developing important topics shaping the future of AI, such as Federated Learning, and cloud-based scientfic networks, which require connectivity and storage capabilities adapted to such purposes. As discussed in the institutional support letter, this vision aligns well with Idiap’s future, its predicted growth (with a current data center reaching the limits of its maximum capacity), and the notion of Cross Research Groups (CRG), that is part of our 2021-2024 Research Program as approved by the Federal State Secretariat for Education, Research, and Innovation (SERI).
Idiap Research Institute
Swiss National Science Foundation
Jun 01, 2023
Dec 31, 2024