Artificial Intelligence
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Increase in co-evolution between hardware and AI

While hardware initially facilitates the computing for AI, it is now becoming the limiting factor, forcing AI developments to fit with computing infrastructure. The growing popularity and accessibility of AI models are driving a significant shift in hardware innovations. AI models are increasing in size and complexity faster than the advancements in general-purpose computing technologies for processing.

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In the previous SURF Tech Trends Report 2023, the surge in specialised AI hardware was highlighted. In addition to this trend, more devices (phones, laptops, etc.) are being produced to support daily AI workflows. Another development driven by this mismatch is the design of AI models to use available computational resources more efficiently. With innovations such as low-precision models and AI-aware hardware implementations, AI is being reshaped, driven by the need to achieve ‘more with less’ in system capabilities.

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

  • Personal AI workstations and increased attention for trusted compute architectures may help mitigate current privacy issues with big tech LLMs, as well as reducing data traffic across networks.
  • High-performance compute for (HPC) for AI will offer students the possibility to handle more complex data tasks, however training may be required.
Research

Research

  • More accessible computer hardware with higher performance (personal supercomputers) means that there will be enhanced research capabilities for the research community.
  • A dependency on a handful of vendors for AI-hardware exposes a risk of a potential scarcity of AI-devices as global supply chains come under pressure.
Operations

Operations

  • Personal AI workstations and more efficient AI models, at lower cost, mean students and researchers are less likely to require large-scale high-performance computing (HPC) systems to experiment with cutting-edge AI. Unlike the computer science, data science, and AI communities who will still need to carry out internationally competitive research.
  • Global demand for resources enabling AI specific chips, could limit hardware availability of edge devices supporting smart campus automation, research and education.
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