Low-Power 3D Nano/CMOS Hybrid Dynamically Reconfigurable Architecture

In order to continue technology scaling beyond CMOS, diverse nanoarchitectures have been proposed in recent years based on emerging nanodevices, such as nanotubes, nanowires, etc. Among them, some hybrid nano/CMOS reconfigurable architectures enjoy the advantage that they can be fabricated using photolithography. NATURE is one such architecture that we have proposed recently. It comprises CMOS reconfigurable logic and CMOS fabrication-compatible nano RAMs. It uses distributed high-density and fast nano RAMs as on-chip storage for storing multiple reconfiguration copies, enabling fine-grain cycle-by-cycle reconfiguration. It supports a highly efficient computational model, called temporal logic folding, which makes possible more than an order of magnitude improvement in logic density and area-delay product, significant power eduction, and significant design flexibility in performing area-delay trade-offs. In this article, we extend NATURE in various dimensions, evaluating various FPGA approaches in the context of today's emerging technologies. First, we explore the introduction of embedded coarse-grain modules in the fine-grain NATURE architecture and present a unified dynamically reconfigurable architecture, which can significantly enhance NATURE's computation power for data-dominated applications. Second, we explore a 3D architecture for NATURE in which the nano RAM for reconfiguration storage is on one layer and the rest of the CMOS logic on another layer. This leads to further improvements in logic density and performance. Finally, we explore the possibility of using FinFETs, an emerging double-gate CMOS technology, to implement NATURE. Since power consumption is an important consideration in the deep nanometer regime, especially for FPGAs, we present a back-gate biasing methodology for flexible threshold voltage adjustment in FinFETs to significantly reduce NATURE's power consumption. Simulation results demonstrate the efficacy of the proposed methods. © 2010 ACM