Theses Doctoral

Design and Optimization of Networks-on-Chip for Future Heterogeneous Systems-on-Chip

Yoon, Young Jin

Due to the tight power budget and reduced time-to-market, Systems-on-Chip (SoC) have emerged as a power-efficient solution that provides the functionality required by target applications in embedded systems. To support a diverse set of applications such as real-time video/audio processing and sensor signal processing, SoCs consist of multiple heterogeneous components, such as software processors, digital signal processors, and application-specific hardware accelerators. These components offer different flexibility, power, and performance values so that SoCs can be designed by mix-and-matching them.
With the increased amount of heterogeneous cores, however, the traditional interconnects in an SoC exhibit excessive power dissipation and poor performance scalability. As an alternative, Networks-on-Chip (NoC) have been proposed. NoCs provide modularity at design-time because
communications among the cores are isolated from their computations via standard interfaces. NoCs also exploit communication parallelism at run-time because multiple data can be transferred simultaneously.
In order to construct an efficient NoC, the communication behaviors of various heterogeneous components in an SoC must be considered with the large amount of NoC design parameters. Therefore, providing an efficient NoC design and optimization framework is critical to reduce the design
cycle and address the complexity of future heterogeneous SoCs. This is the thesis of my dissertation.
Some existing design automation tools for NoCs support very limited degrees of automation that cannot satisfy the requirements of future heterogeneous SoCs. First, these tools only support a limited number of NoC design parameters. Second, they do not provide an integrated environment for software-hardware co-development.
Thus, I propose FINDNOC, an integrated framework for the generation, optimization, and validation of NoCs for future heterogeneous SoCs. The proposed framework supports software-hardware co-development, incremental NoC design-decision model, SystemC-based NoC customization and generation, and fast system protyping with FPGA emulations.
Virtual channels (VC) and multiple physical (MP) networks are the two main alternative methods to provide better performance, support quality-of-service, and avoid protocol deadlocks in packet-switched NoC design. To examine the effect of using VCs and MPs with other NoC architectural
parameters, I completed a comprehensive comparative analysis that combines an analytical model, synthesis-based designs for both FPGAs and standard-cell libraries, and system-level simulations.
Based on the results of this analysis, I developed VENTTI, a design and simulation environment that combines a virtual platform (VP), a NoC synthesis tool, and four NoC models characterized at different abstraction levels. VENTTI facilitates an incremental decision-making process with four
NoC abstraction models associated with different NoC parameters. The selected NoC parameters can be validated by running simulations with the corresponding model instantiated in the VP.
I augmented this framework to complete FINDNOC by implementing ICON, a NoC generation and customization tool that dynamically combines and customizes synthesizable SystemC components from a predesigned library. Thanks to its flexibility and automatic network interface generation
capabilities, ICON can generate a rich variety of NoCs that can be then integrated into any Embedded Scalable Platform (ESP) architectures for fast prototying with FPGA emulations.
I designed FINDNOC in a modular way that makes it easy to augmenting it with new capabilities. This, combined with the continuous progress of the ESP design methodology, will provide a seamless SoC integration framework, where the hardware accelerators, software applications, and
NoCs can be designed, validated, and integrated simultaneously, in order to reduce the design cycle of future SoC platforms.


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More About This Work

Academic Units
Computer Science
Thesis Advisors
Carloni, Luca
Ph.D., Columbia University
Published Here
March 22, 2017