Research & development - Leuven | More than two weeks ago
Design Space Exploration for High Performance AIMC Based Accelerator
With the massive growth in size of neural networks, energy consumption of neural network inference has also risen. From a computation perspective, quantization techniques are used widely for lowering computational complexity. From the hardware perspective, analog-in-memory computing (AiMC) based solutions have grown popular in recent years. AiMC based solutions have extremely high energy efficiency of the order of 1000Tops/W.
The performance of an accelerator is heavily dependent on the choice of design parameters as well the technology parameters. Given the wide range of choices available for these parameters, it becomes imperative to systematically determine which design parameters have the highest impact overall accelerator efficiency.
The specific goals of this project are as follows:
Mandatory: Computer Architecture, Python, familiarity with deep neural networks.
Optional: Pytorch, Familiarity with NN Accelerators
Type of project: Internship, Thesis
Duration: 6 months
Required degree: Master of Engineering Technology, Master of Science, Master of Engineering Science
Required background: Computer Science, Electrotechnics/Electrical Engineering
Supervising scientist(s): For further information or for application, please contact: Debjyoti Bhattacharjee (Debjyoti.Bhattacharjee@imec.be) and Peter Debacker (Peter.Debacker@imec.be) and Arindam Mallik (Arindam.Mallik@imec.be)
Imec allowance will be provided for students studying at a non-Belgian university.