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

There is great interest in "end-to-end" analysis that captures how innovation at the materials, device, and/or architectural 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.