The 2022 ACM/IEEE International Workshop on System-Level Interconnect Pathfinding (SLIP) is the 24th edition of the Workshop.

SLIP, co-located with ICCAD 2022, will bring together researchers and practitioners who have a shared interest in the challenges and futures of system-level interconnect, coming from wide-ranging backgrounds that span system, application, design and technology.

The technical goal of the workshop is to

  1. identify fundamental problems, and,
  2. foster new pathfinding of design, analysis, and optimization of system-level interconnects with emphasis on system-level interconnect modeling and pathfinding, DTCO-enhanced interconnect fabrics, memory and processor communication links, novel dataflow mapping for machine learning, 2.5/3D architectures, and new fabrics for the beyond-Moore era.
Original submissions in the form of regular technical papers, invited sessions (tutorials, panels, special-topic sessions), workshop discussion topics, and posters are welcome. Program content is accepted based on novelty and contributions to the advancement of the field. Accepted technical papers will be published in the ACM and IEEE digital libraries.



Technical topics include but are not limited to:

  • Learning and predictive models for interconnect at various IC and system design stages
  • Roadmapping and pathfinding of on-chip interconnect and 2.5D/3D chip-to-chip communication interfaces
  • System-level design for FPGAs, NoCs reconfigurable systems, and domain-specific multi/many-core systems
  • Design, analysis, and (co)optimization of power, clock distribution networks, and memory partitioning systems
  • System-level interconnect reliability, aging, thermal, yield and cost issues
  • Predictive models for power and performance of system-level interconnects
  • Interconnects in bio-inspired systems, such as artificial neural networks and quantum architectures



    There are two special sessions this year:

    1. Power distribution in advanced technology nodes
    2. Tackling memory wall in many-core and AI systems