/Exploration and Characterization of Applications Near Memory

Exploration and Characterization of Applications Near Memory

Master projects/internships - Leuven | More than two weeks ago

Identifying HPC-class targets for emerging compute-near-memory architectures.

Systems that deploy computational logic near memory can overcome typical von Neumann-based bottlenecks (e.g. memory wall) by limiting the amount of data transferred to central compute areas in a system. With modern compute-near memory (CnM) systems still in their infancy and typical programming paradigms focused on centralized computing however, new work must be undertaken to identify, implement, and evaluate new software for CnM hardware.

During this research internship, you will find distributed computation targets by becoming acquainted with modern datacentre-class algorithms and applications, learning about application characterization, and using state-of-the-art methodologies and tools to identify memory-based bottlenecks. Your work will build the foundation for a framework with which modern “typical” applications can become CnM-enabled applications and will be vital for guiding CnM hardware development.

Key responsibilities will include:

  • Conducting detailed studies into state-of-the-art datacentre-class applications and associated accelerators, including but not limited to applications and accelerators for AI, Genomics, Databases, and Forecasting.
  • Analysing, disseminating, proposing, and implementing near-memory algorithms.
  • Collaborating with technologists to propose and implement features and constraints in CnM hardware.
  • Implementing software libraries to ease the integration of applications into CnM hardware.
  • This role is ideal for someone who is deeply interested in hardware-software codesign, software engineering, and working in an interdisciplinary environment that values innovation, creativity, and real-world impact.

Profile: You are analytical and detail-oriented, with a strong interest in AI, genomics, databases, forecasting, or other datacentre-class algorithms. You are adept at or have a keen interest in programming, static and dynamic analysis, and performance evaluation tools.

Background: Currently pursuing or already have a degree in computer engineering, computer science, informatics, or electrical engineering. Has some background in algorithms, parallel programming, or accelerators. Knowledge of object-oriented programming and scripting languages is an advantage.

Type of project: Internship, Thesis

Required degree: Master of Science, Master of Engineering Science, Master of Engineering Technology

Required background: Computer Science, Electrotechnics/ Electrical Engineering

Duration: 3 to 9 months

Supervising scientist(s): For more information on this topic, please contact Leandro M. Giacomini Rocha  (leandro.m.giacominirocha@imec.be) and Joshua Klein (joshua.klein@imec.be).

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