Master projects/internships - Leuven | More than two weeks ago
Enabling physical aware power modeling in high-level simulation frameworks for Machine Learning Accelerators
Deep learning accelerators are an integral part of most modern compute system architectures. This work involves performance and power modeling of such a system in a full-system context via virtual platform modeling & simulation. This enables early design space exploration of the system at the early stage of full system design based on the PPA metrics. Performance modeling at the system level would be a very important part of this procedure. The goal of this project is to enable integrating Machine Learning (ML) accelerator in a virtual platform. In the first phase of this project, the accelerator RTL model will be integrated in the simulation framework, and various ML applications will be validated within the full system. In the second phase of this project a transaction-level model (SystemC/TLM) of the accelerator will be replaced with the RTL model to provide faster system evaluation and finding the capabilities of fast modeling. The last phase will be integrating the accelerator power model in parallel with the TLM model to have fast power estimation at the system-level.
Objectives:
Skills:
Type of Project: Combination of internship and thesis
Master's degree: Master of Engineering Technology; Master of Engineering Science
Master program: Electrotechnics/Electrical Engineering; Computer Science
Duration: 6-9 months
For more information or application, please contact Katayoon Basharkhah (katayoon.basharkhah@imec.be).
Imec allowance will be provided.