Design Space Exploration and Data Memory Architecture Design for a Hybrid Nano/CMOS Dynamically Reconfigurable Architecture

摘要

In recent years, research on nanotechnology has advanced rapidly. Novel nanodevices have been developed, such as those based on carbon nanotubes, nanowires, etc. Using these emerging nanodevices, diverse nanoarchitectures have been proposed. Among them, hybrid nano/CMOS reconfigurable architectures have attracted attention because of their advantages in performance, integration density, and fault tolerance. Recently, a high-performance hybrid nano/CMOS reconfigurable architecture, called NATURE, was presented. NATURE comprises CMOS reconfigurable logic and interconnect fabric, and CMOS-fabrication-compatible nanomemory. High-density, fast nano RAMs are distributed in NATURE as on-chip storage to store multiple reconfiguration copies for each reconfigurable element. It enables cycle-by-cycle runtime reconfiguration and a highly efficient computational model, called temporal logic folding. Through logic folding, NATURE provides more than an order of magnitude improvement in logic density and area-delay product, and significant design flexibility in performing area-delay trade-offs, at the same technology node. Moreover, NATURE can be fabricated using mainstream photolithography fabrication techniques. Hence, it offers a currently commercially viable reconfigurable architecture with high performance, superior logic density, and outstanding design flexibility, which is very attractive for deployment in cost-conscious embedded systems. In order to fully explore the potential of NATURE and further improve its performance, in this article, a thorough design space exploration is conducted to optimize its architecture. Investigations in terms of different logic element architectures, interconnect designs, and various technologies for nano RAMs are presented. Nano RAMs can not only be used as storage for configuration bits, but the high density of nano RAMs also makes them excellent candidates for large-capacity on-chip data storage in NATURE. Many logic- and memory-intensive applications, such as video and image processing, require large storage of temporal results. To enhance the capability of NATURE for implementing such applications, we investigate the design of nano data memory structures in NATURE and explore the impact of memory density. Experimental results demonstrate significant throughput improvements due to area saving from logic folding and parallel data processing.