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Cross Layer Design for the Predictive Assessment of Technology-Enabled Architectures

Cornell Affiliated Author(s)


M. Niemier
X. Hu
L. Liu
M. Sharifi
I. O’Connor
D. Atienza
G. Ansaloni
C. Li
A. Khan
D. Ralph


There is great interest in “end-to-end” analysis that captures how innovation at the materials, device, and/or archi-tectural levels will impact figures of merit at the application-level. However, there are numerous combinations of devices and architectures to study, and we must establish systematic ways to accurately explore and cull a vast design space. We aim to capture how innovations at the materials/device-level may ultimately impact figures of merit associated with both existing and emerging technologies that may be employed for either logic and/or memory. We will highlight how collaborations with researchers at these levels of the design hierarchy - as well as efforts to help construct well-calibrated device models - can in-turn support architectural design space explorations that will help to identify the most promising ways to use new technologies to support application-level workloads of interest. For given compute workloads, we can then quantitatively assess the potential benefits of technology-driven architectures to identify the most promising paths forward. Because of the large number of potentially interesting device-architecture combinations, it is of the utmost importance to develop well-calibrated analytical modeling tools to more rapidly assess the potential value of a given (likely heterogeneous) solution. We highlight recent efforts and needs in this space.

Date Published

Conference Name

2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)

ISBN Number




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