Gene Expression Networks (GENs) attempt to model how genetic information stored in the DNA (Genotype) results in the synthesis of proteins, and consequently, the physical traits of an organism (Phenotype). Deciphering GENs plays an important role in a wide range of applications from genetic studies of the origins of life to personalized healthcare. Probabilistic graphical models such as Bayesian Networks (BNs) are used to perform learning and inference of GENs from genetic data.
Parallel and monolithic 3D integration directions realize 3D integrated circuits (ICs) by utilizing layer-by-layer implementations, with each functional layer being composed in 2D. In contrast, vertically-composed 3D CMOS has eluded us likely due to the seemingly insurmountable requirement of highly customized complex routing and regional 3D doping to form and connect CMOS pull-up and pull-down networks in 3D. In the current layer-by-layer directions, routing can be worse than 2D CMOS because of the limited pin access.
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.
Conventional 2D CMOS technology is reaching fundamental scaling limits, and interconnect bottleneck is dominating integrated circuit (IC) power and performance. While 3D IC technologies using Through Silicon Via or Monolithic Inter-layer Via alleviate some of these challenges, they follow a similar layout and routing mindset as 2D CMOS. This is insufficient to address routing requirements in high-density 3D ICs and even causes severe routing congestion at large-scale designs, limiting their benefits and scalability.
Parallel and monolithic 3D integration directions offer pathways to realize 3D integrated circuits (ICs) but still lead to layer-by-layer implementations, each functional layer being composed in 2D first. This mindset causes challenging connectivity, routing and layer alignment between layers when connected in 3D, with a routing access that can be even worse than 2D CMOS, which fundamentally limits their potential.
Continuous scaling of CMOS has been the major catalyst in miniaturization of integrated circuits (ICs) and crucial for global socio-economic progress. However, scaling to sub-20nm technologies is proving to be challenging as MOSFETs are reaching their fundamental limits and interconnection bottleneck is dominating IC operational power and performance. Migrating to 3-D, as a way to advance scaling, has eluded us due to inherent customization and manufacturing requirements in CMOS that are incompatible with 3-D organization.
This paper introduces a new fine-grained 3D IC fabric technology called NP-Dynamic Skybridge. Skybridge is a family of 3D IC technologies that provides fine-grained vertical integration. In comparison to the original 3D Skybridge, the NP-Dynamic approach enables a more comprehensive logic style for improved efficiency. It addresses device, circuit, connectivity and manufacturability requirements with an integrated 3D mindset. The NP-Dynamic 3D circuit style enables wide range of logic expressions, simple clocking scheme, and reduces buffer requirements.
At Sub-20nm technologies CMOS scaling faces severe challenges primarily due to fundamental device scaling limitations, interconnection overhead and complex manufacturing. Migration to 3-D has been long sought as a possible pathway to continue scaling; however, CMOS’s intrinsic requirements are not compatible for fine-grained 3-D integration. In , we proposed a truly fine-grained 3-D integrated circuit fabric called Skybridge that solves nanoscale challenges and achieves orders of magnitude benefits over CMOS.
In this chapter, we introduce a new fully generic computational paradigm for post-CMOS integrated circuits based on emerging wave-like physical phenomenon (e.g. spin waves), called Wave Interference Functions (WIF). Waves offer new features and opportunities for logic circuits with inherent support for multi-valued data representation, communication and computation. Multi-valued information processing occurs through wave interference, and multi-valued communication between processing elements is through wave propagation.
Neuromorphic computing mimicking the functionalities of mammalian brain holds the promise for cognitive capabilities enabling new intelligent applications. However, research efforts so far mainly focused on using analog and digital CMOS technologies to emulate neural activities, and are yet to achieve expected benefits. They suffer from limited scalability, density overhead, interconnection bottleneck and power consumption related constraints. In this paper, we present a transformative approach for neuromorphic computing with Wave Interference Functions (WIF).