Enabling workload-driven novel reconfigurable systems leveraging system technology co-optimisation.

With the increasing diversity of domains using AI/ML-basedapplications, there is a growing diversity of workload requirements fromelectronic systems. While heterogeneous computing systems (CPU, GPU, and NPUaccelerators) provide some level of optimization, the static nature ofcomputation, communication, and memory elements limits the level of utilizationfor emerging workloads. Similarly, state-of-the-art reconfigurable systems(FPGAs, CGRAs, etc.) focus on runtime reconfiguration targeted at functionalitymodifications only.

The student will be working on a workload-aware explorationframework. The project will involve identifying system-level designs that canleverage existing reconfigurable technologies, across computation,communication, and memory, to provide optimized PPAC (power, performance, area,and cost) trade-offs for a set of workloads.

Type of project: Internship only

Type of work: 10% literature survey togain anunderstanding of the landscape of technology innovations and stat-of-the-artreconfigurable technologies. 90% hands-on modelling and framework developmentfor driving the design and exploration of technology-architecture co-design.

Type of internship: Master internship

Duration: 3-6 months

Required educational background: Computer Science, Electrotechnics/Electrical Engineering

Supervising scientist(s): For further information or for application, please contact Siva Satyendra Sahoo (< email deleted for security reasons >)

The reference code for this position is 2026-INT-019. Mention this reference code in your application.

Imec allowance will be provided for students studying at a non-Belgian university.

Applications should include the following information:

• resume
• motivation
• current study

Incomplete applications will not be considered.