Leuven | More than two weeks ago
As part of this work, we aim to develop analytical models that help us to benchmark and compare system level power, performance, area benefits resulting from 3D scaling for mobile/AI SoCs while executing compute-memory bound applications. The student is expected to work with state-of-the-art analytical models and benchmark popular DNN application workloads on different SoCs. The outcomes will help to form direct comparison with physical design parameters obtained from logic synthesis, Place and Route. In the process, the student is expected to closely work with system architects on exploring various memory/logic partitioning schemes.
Type of project: Internship, Thesis, Combination of internship and thesis
Duration: 6-9 months
Required degree: Master of Engineering Technology, Master of Science, Master of Engineering Science
Required background: Electrotechnics/Electrical Engineering, Computer Science, Physics
Supervising scientist(s): For further information or for application, please contact: Dwaipayan Biswas (Dwaipayan.Biswas@imec.be) and Saikat Chatterjee (Saikat.Chatterjee@imec.be) and Priya Venugopal (Priya.Venugopal@imec.be)
Imec allowance will be provided.