09/06/2020

Research Fellow / PhD Student (f/m/d) Approximate Computing for Deep Neural Networks

This job offer has expired


  • ORGANISATION/COMPANY
    Karlsruhe Institute of Technology (KIT)
  • RESEARCH FIELD
    Computer science
    Engineering
    Technology
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
    Recognised Researcher (R2)
    Established Researcher (R3)
    Leading Researcher (R4)
  • APPLICATION DEADLINE
    06/07/2020 23:59 - Europe/Brussels
  • LOCATION
    Germany › Karlsruhe
  • TYPE OF CONTRACT
    Other
  • JOB STATUS
    Full-time

OFFER DESCRIPTION

Area of research:

Scientific / postdoctoral posts



Starting date:

1591567200



Job description:


In the Department of computer science at the Institute of Computer Engineering, Chair for Embedded Systems (CES), we focus our research on neural network, reliability, and emerging technologies. As a matter of fact, computing systems have reached a point, where significant improvements in computational performance and efficiency have become very hard to achieve. The main reasons are power (density) and efficiency limitations due to the discontinuation of Dennard Scaling as well as increased reliability concerns. Approximate Computing trades off precision against power, energy, storage, bandwidth or performance, and can be applied to hardware, software and algorithms. It promises to re-gain efficient computing by providing additional, adjustable design and runtime parameters to find pareto-optimal solutions. Neural network domain is one of the primary candidates when it comes to approximate computing due to the nature of neural networks which are inherently error tolerant. Accelerating the training and inference of neural network is currently on top of the research focus of both academia and industry.


Expertise in one or more of the following areas is recommended


Hardware accelerators for neural networks.Approximate Computing across the stack, from circuit level through micro-architecture and system-level.Modeling the trade-offs in hardware accelerators. Analyzing the resiliency of various neural under different applied approximate computing means.Architectural Resiliency of Neural Networks.



More Information

Work location(s)
1 position(s) available at
Karlsruhe Institute of Technology (KIT)
Germany
Karlsruhe

EURAXESS offer ID: 530828
Posting organisation offer ID: 726883

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