Artificial Intelligence is becoming ubiquitous in products and services that we use daily. Although the domain of AI has seen substantial improvements over recent years, its effectiveness is limited by the capabilities of current computing technology. Recently, there have been several architectural innovations for AI using emerging nanotechnology. These architectures implement mathematical computations of AI with circuits that utilize physical behavior of nanodevices purpose-built for such computations. This approach leads to a much greater efficiency vs.

Our European Union’s Horizon-2020 project aims to develop a complete synthesis and performance optimization
methodology for switching nano-crossbar arrays that leads to the design and construction of an emerging nanocomputer.
Within the project, we investigate different computing models based on either two-terminal switches, realized with field effect

Nano-crossbar arrays have emerged to achieve high performance computing beyond the limits of current CMOS. They offer area and power efficiency in courtesy of their easyto-fabricate and dense physical

Non-volatile 3D FPGA research to date utilizes layer-by-layer stacking of 2D CMOS / RRAM circuits. On the other hand, vertically-composed 3D FPGA that integrates CMOS and RRAM circuits has eluded us, owing to the difficult requirement of highly customized regional doping and material insertion in 3D to build and route complementary p- and n-type transistors as well as resistive switches. In the layer-by-layer nonvolatile 3D FPGA, the connectivity between the monolithically stacked RRAMs and underlying CMOS circuits is likely to be limited and lead to large parasitic RCs.

Thermal management is one of the critical challenges in 3D integrated circuits. Incorporating thermal optimizations during the circuit design stages requires a convenient automatic method of doing thermal characterization for feedback purposes. In this paper, we present a methodology, which supports thermal characterization by automatically extracting the steady-state thermal modeling resistance network from a post-placement physical design. The method follows a two-level hierarchical approach.