Important Dates
- Submission Deadline: 21 February 2023
- Major Revisions Due: 1 June 2023
- Notification of Final Acceptance: 15 July 2023
- Final Manuscripts Due: 15 August 2023
• Publication: 1 October 2023
Many interacting computing technology trends call for a change in computer architectures to meet the demands of emerging applications. First, Moore’s Law, which has provided an exponential performance growth through decades, is losing steam. Second, the application landscape of computing is shifting from being compute- to becoming data-intensive. Data intensive applications, e.g., machine learning, graph processing and in-memory database processing impose an increased burden on the classical separation of compute and memory popularly called the von Neumann bottleneck. Fortunately, emerging memory technologies have made it timely to reconsider processing near-memory or in-memory, pioneered already in the 1960s. With the advent of 3D stacked memory, it is possible to implement compute engines or accelerators on a logic die tightly coupled with several layers of memory – near-memory processing. The research community is also considering embedding processing support into the memory arrays in conventional DRAM technology as well as in emerging memory technologies, such as resistive memory – in-memory processing.
This special issue aims at taking a snapshot of ongoing developments in near- and in-memory processing. Challenges are enormous and cut across all compute layers and calls for co-design of circuit-level techniques via system architecture to the software stack. The scope of the special issue is including all these topics and include, among others, the following
- Near/in-memory architecture models
- Near/in-memory acceleration of data intensive applications
- Emerging memory technologies for near/in-memory processing
- System integration of conventional and near/in-memory components
- System support for near/in-memory processing systems
- Programming models and software implications of near/in-memory processing systems
- Evaluation of industry/academic near/in-memory systems
- Security, dependability, power/energy and performance implications of near/in-memory processing systems
- Mapping of emerging killer applications on near/in-memory processing systems
Submission Guidelines
For author information and guidelines on submission criteria, please visit the IEEE TC’s Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
Questions?
Please contact the lead guest editor at per.stenstrom@chalmers.se.
Guest Editors:
- Prof. Onur Mutlu, ETHz
- Prof. Per Stenstrom, Chalmers University of Technology
Review Board:
- Shaizeen Aga, AMD Research
- Jung Ho Ahn, Seoul National University
- Rajeev Balasubramonian, University of Utah
- Jeronimo Castrillon, Technical University Dresden
- Yiran Chen, Duke University
- Caiwen Ding, University of Connecticut
- Ronald Dreslinski, University of Michigan
- Mattan Erez, University of Texas, Austin
- Deliang Fan, Arizona State University
- Saugata Ghose, University of Illinois, Urbana Champaign
- Christina Giannoula, National Technical University of Athens/ University of Toronto
- Juan Gomez Luna, ETH Zurich
- Myoungsoo Jung, KAIST
- Ulya R. Karpuzcu, University of Minnesota
- Nam Sung Kim, University of Illinois, Urbana Champaign/Samsung
- Jae W. Lee, Seoul National University
- Yingyan (Celine) Lin, Georgia Institute of Technology
- David Novo, LIRMM
- Tony Nowatzki, University of California, Los Angeles
- Jisung Park, POSTECH
- Minsoo Rhu, KAIST
- Abu Sebastian, IBM Research
- Vivek Seshadri, Microsoft Research
- Anand Sivasubramaniam, Penn State University
- Kevin Skadron, University of Virginia
- Pedro Trancoso, Chalmers University of Technology
- T. N. Vijaykumar, Purdue University
- Yanzhi Wang, Northeastern University
- Jun Yang, University of Pittsburgh
- Leonid Yavits, Bar-Ilan University
- Sungjoo Yoo, Seoul National University
