Computing
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Increasing need for heterogeneous and hybrid computing platforms

The demands of modern computing applications have effectively challenged the era of classical approaches. Modern large-scale computing environments are increasingly becoming modular, integrating a wide range of specialisations such as hybrid quantum systems, cloud platforms, ASICs, GPUs, edge IoT devices, and storage, enabling the execution of complex tasks. The slowdown of Moore’s Law, coupled with the growing demands of AI-driven design, necessitates specialised solutions for improved outcomes. Although the landscape remains fragmented, tools that facilitate orchestration across heterogeneous environments are becoming more widespread and valuable in mainstream scientific computing. We envision a lot of innovation in the software layers of the digital infrastructure stack for the coming decades.

Maturity

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More diversified access to large AI models Increase in co-evolution between hardware and AI Collaboration between humans and AI Democratisation of XR content creation using generative AI Public investments in XR continue to increase Standardisation of data space architectures for secure and trusted data sharing Growing significance of augmented data management Trusted digital recognitions are emerging Digital Trust Frameworks: from hierarchical to distributed trust Emerging dualuse of AI in cybersecurity More accessible privacy-enhancing technologies to share data Rising pressure to prepare for post-quantum cryptography Emergence of measures for secure Internet of Things The growing need for security protocols to work in the cloud Growing investments in the European cloud infrastructure & sovereignty More AI-driven hardware in data centres to manage workloads in the cloud Increasing need for heterogeneous and hybrid computing platforms Trade-offs in accuracy with approximate computing Fixed wireless access Fibre optic networks: strategic infrastructure for the knowledge economy Enhancing quantum computing robustness and potential Quantum sensing integration to other domains

Drivers

Impact

education

Education

  • Curricula are likely to increasingly embed specialised hardware concepts alongside conventional architectures. Students are therefore likely to engage further with a variety of accelerators dedicated to certain tasks in cloud lab environments. Lectures, labs, and projects will train students to deploy and optimise workloads on specialised accelerators. This shift is likely to drive academic programs to evolve beyond foundational programming courses towards more advanced parallel and heterogeneous computing paradigms. As a result, curricula will have to increasingly incorporate models such as CUDA, HIP, SYCL, or OpenCL.
Research

Research

  • Scientific applications are likely to run more often on heterogeneous systems, such as “GPU for dense linear algebra, FPGA for bit-level genomics, or neural processing unit (NPU) for inference,” within a single cluster job. This shortens iteration cycles, enables larger parameter sweeps, and lowers energy budgets. Hardware-software co-design is set to become a fertile ground, inspiring resource-efficient algorithms and spurring rapid innovation in chip architectures. However, new scheduling policies and heterogeneous-aware middleware from campus HPC centres are needed as a result.
Operations

Operations

  • Operation teams might need to assess designs that combine different components while balancing power and cooling needs. Furthermore, facility layouts, monitoring tools, and support staff skills will need to adapt to efficiently manage mixed-architecture racks and the fast-evolving generations of accelerators.
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