Open positions

The PhD project aims to develop a neural architecture search (NAS) framework for designing an efficient, robust, and reliable hybrid CNN-Transformer supernetwork capable of generating distinct subnetworks specialised for various HW platforms without extensive retraining. The research objectives include: a) investigating existing and introducing novel training algorithms to enhance the robustness, reliability, and accuracy of the subnetworks within the supernetwork; b) developing fast and efficient search engine algorithms for extracting subnetworks from the trained supernetwork; c) training surrogate predictor models to evaluate key metrics such as accuracy, robustness, reliability, and latency for full-precision and quantised sub-networks. This PhD position is one of the 17 positions in the European Marie Skłodowska-Curie Action Doctoral network "TIRAMISU - Training and Innovation in Reliable and Efficient Chip Design for Edge AI" (2024-2028).

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The PhD project aims at novel, low-cost, ultra-low power open-source RISC-V-based accelerators augmented with reliability and safety features and tailored for edge AI applications. Energy efficiency is to be addressed by optimisation of the architecture, memory communication and the control. The lifetime and soft-error reliability aspect assumes architectural and physical design methodologies, as well as system-level improvements. For safety, the development of an infrastructure for in-field fault management of RISC-V-based systems to prevent catastrophic system failures, including fault and ageing detection and recovery mechanisms, using IJTAG for embedded instruments. This PhD position is one of the 17 positions in the European Marie Skłodowska-Curie Action Doctoral network "TIRAMISU - Training and Innovation in Reliable and Efficient Chip Design for Edge AI" (2024-2028).

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