KU LEUVEN

Postdoc in Chip Design for Efficent Embedded Machine Learning Processors

7 days left

Employer
KU LEUVEN
Location
Leuven, Louvain (BE)
Posted
Jul 10, 2020
End of advertisement period
Aug 21, 2020
Contract Type
Fixed Term
Hours
Full Time

POST-DOC IN CHIP DESIGN FOR EFFICIENT EMBEDDED MACHINE LEARNING PROCESSORS

(ref. BAP-2020-544)

The postdoc will operate in a collaboration between the research group MICAS (KU Leuven) and the imec research centre. MICAS is a research group within the department of Electrical Engineering (ESAT) of the University of Leuven (KU Leuven). MICAS conducts research on the design of integrated circuits in nano-electronic technologies. This research is performed in close collaboration with the semiconductor industry and about 75% of the groups’ budget is through industrial collaborations. MICAS consists of 7 professors and 80 Ph.D. researchers. As such, KU Leuven MICAS is the largest academic circuit-level research group within Europe. MICAS tapes out dozens of chips every year, and possesses a top notch chip bonding, packaging and measurement lab. More information about the group and its activities can be found at http://www.esat.kuleuven.be/micas/ Interuniversity Microelectronics Centre (imec) is an international research & development and innovation hub, active in the fields of nanoelectronics and digital technologies. imec is the largest European research center in the field of microelectronics, nanotechnology, artificial intelligence, design methods and technologies for ICT systems. More information at: https://www.imec-int.com/en/home

Responsibilities

The post doctoral researcher will conduct and supervise research on resource-efficient digital and mixed-signal implementations of machine learning processors, focused around deep learning.

Recently deep neural networks have gained enormous popularity in the signal processing community. In the micro-electronics research domain this has sprouted attention on customized processors for efficient embedded deep neural network inference. Our team has

published several of these state-of-the-art processors over the past few years. We will build upon our work in the field of deep learning processors and hardware/algorithm co-optimization, which has demonstrated to save orders of magnitude on energy efficiency. Yet, we want to extend this work towards a.) new application domains (such as acoustics, health monitoring and sensor fusion); b.) online learning for user customization; c.) mixed-signal processing and compute-in-memory.

You will closely collaborate with researchers from KU Leuven’s algorithmic machine learning group, researchers from Imec, researchers from various international research groups and several partner companies. You will have ample opportunities to interact with top research institutes and important industrial players in the field.

Profile

  •  Candidates must hold a PhD degree in Electrical Engineering (or equivalent), with a background in digital design, mixed-signal design or processor design. 
  •  Additional research/educational experience in computer architectures, machine learning and chip tape out are a strong plus. 
  •  We are looking for a team player with the capability to work in an international research team. 
  •  Excellent proficiency in the English language is also required, as well as good communication skills, both oral and written

Offer

  •  An exciting  research environment, working on the intersection between emerging research domains (machine learning; processor design; ultra-low power chip and system design)
  •  The ability to closely interact with our industrial partners, as well as many academic research partners in ongoing projects
  •  A thorough scientific education, the possibility to become a world-class researcher 
  •  A KU Leuven affiliation, one of the largest research universities of Europe
  •  The possibility to participate in international conferences and academic as well as industrial collaboration

Interested?

For more information please contact Prof. dr. ir. Marian Verhelst, tel.: +32 16 32 86 17, mail: marian.verhelst@kuleuven.be.

You can apply for this job no later than August 21, 2020 via the online application tool

KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.

Similar jobs

Similar jobs