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. 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: 1) finding a suitable neural architecture, compressed-enough to fit the constraints of special hardware, 2) achieving multi-stage cross-compilation of critical logic and ML functions and, 3) equipping critical hardware with proper runtime support in order to actuate to data-application demands without sacrificing safety and service time guarantees. 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.

[Show all publications]

Autonomous Realization of Safety-and Time-Critical Embedded Artificial Intelligence (Mar 2024)
Joakim Lindén, Andreas Ermedahl, Hans Salomonsson , Masoud Daneshtalab, Bjorn Forsberg , Paris Carbon
Design, Automation & Test in Europe Conference (DATE'24)

A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks (Jan 2024)
Mohammad Ahmadilivani , Mahdi Taheri , Jaan Raik , Masoud Daneshtalab, Maksim Jenihhin
ACM Computing Surveys (CSUR)

Analysis and Improvement of Resilience for Long Short-Term Memory Neural Networks (Oct 2023)
Mohammad Ahmadilivani , Jaan Raik , Masoud Daneshtalab, Alar Kuusik
36th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT 2023)

Efficient On-device Transfer Learning using Activation Memory Reduction (Sep 2023)
Amin Yoosefi , Seyedhamidreza Mousavi, Masoud Daneshtalab, Mehdi Kargahi
International Conference on Fog and Mobile Edge Computing (FMEC)

Enhancing Fault Resilience of QNNs by Selective Neuron Splitting (Jun 2023)
Mohammad Ahmadilivani , Javid Taheri , Jaan Raik , Maksim Jenihhin , Masoud Daneshtalab
5th IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) (AICAS 2023)

NeuroPIM: Flexible Neural Accelerator for Processing-in-Memory Architectures (May 2023)
Ali Monavari , Sepideh Fattahi , Mehdi Modarressi , Masoud Daneshtalab
International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)

KTH Royal Institute of Technology Academic
RISE Research Institutes of Sweden Academic
EmbeDL AB Industrial
Ericsson AB Industrial
Saab Industrial

Masoud Daneshtalab, Professor

Phone: +4621103111