FASTER ΑΙ: Fully Autonomous Safety- and Time-critical Embedded Realization of Artificial Intelligence
FASTER AI addresses emergent needs to embed machine learning (ML) inference capabilities within hardware infrastructure of critical importance and use.
Concluded
Project website
Start
2023-01-01
Conclusion
2025-01-01
Main financing
Research group
Project manager at MDU
FASTER AI addresses emergent needs to embed machine learning (ML) inference capabilities within hardware infrastructure of critical importance and use. We focus on hardware utilized widely in telecommunications as well as airborne systems and other vehicles. Current ML workflow programming tools are controlled primarily by dominant cloud vendors and overlook non-commodity use, focusing solely on standard AI accelerators. However, as ML inference takes over traditional heuristic- and control-based decision-making in the industry there are major needs to re-purpose that hardware towards the use of ML.
Driven by use cases of safety- and time-critical functions, we streamline our ML integration pipeline around three core activities:
- finding a suitable neural architecture, compressed-enough to fit the constraints of special hardware
- achieving multi-stage cross-compilation of critical logic and ML functions
- equipping critical hardware with proper runtime support in order to actuate to data-application demands without sacrificing safety and service time guarantees.
Project objectives
Our methodology is effective for current hardware but also future-proof for upcoming architectures or releases of special accelerators used in critical decision-making industries. We strongly believe that the FASTER AI approach is the most sustainable way forward toward digitalizing and creating value out of our existing critical infrastructures while also maintaining a relevant outlook for the future.
