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doctoral thesis

Leading the Blind : Automated Transistor-Level Modeling for FPGA Architects

Zgheib, Grace  
2017

The design and development of innovative FPGA architectures hinge on the flexibility of its toolchain. Retargetable toolchains, like the Verilog-to-Routing (VTR) flow, have been developed to enable the testing of new FPGAs by mapping circuits onto easily-described and possibly theoretical architectures. However, in reality, the difficulty extends beyond having CAD tools that support the architectural changes: it is equally important for FPGA architects to be able to produce reliable delay and area models for these tools. In addition to having acute architectural intuitions, designing and optimizing the circuit at the transistor-level requires architects to have, as well, a particular set of electrical engineering skills and expertise. The process is also painstaking and time-consuming, rendering the comparison of a variety of architectures or the exploration of a wide design space quite complicated and even impossible in practice. In this work, we present a novel approach to model the delay and area of FPGA architectures with various structures and characteristics, quickly and with acceptable accuracy. Abstracting from the user the transistor-level design and optimization that normally accompany the model- ing process, this approach, called FPRESSO, can be used by any architect without prerequisites. We take inspiration from the way a standard-cell flow performs large-scale transistor-size optimization and apply the same concepts to FPGAs, only at a coarser granularity. Skilled designers prepare for FPRESSO a set of locally optimized libraries of basic parameterizable components with a variety of drive strengths. Then, inexperienced users specify arbitrary FPGA architectures as interconnects of these basic components. The architecture is globally optimized, within minutes, through a standard logic synthesis tool, by choosing the most fitting version of each cell and adding buffers wherever appropriate. The resulting delay and area characteristics are automatically returned, in a format suitable for the VTR flow. A correct modeling of any architecture requires not only an optimization of the logic components, but also a proper modeling of the wires connecting these components. This does not only include measuring the length of the wires to determine their respective resistance and capacitance, but also, minimizing their length to reduce the wireload effect on the overall performance. To that end, FPRESSO features an automatic and generic wire modeling approach based on a simulated annealing floorplanning algorithm, to estimate the wires between the different components of the FPGA architecture. To evaluate the results of FPRESSO and confirm the validity of its modeled architectures, we use it to explore a wide range of FPGA architectures. First, we repeat a known study that helped set the standards on the optimal Look-Up-Table (LUT) and cluster size for conventional FPGAs. We show, by comparing with the results of the study, that modeling in FPRESSO preserves the very same trends and conclusions, with significantly less effort. We then extend the search space to cover fracturable LUTs and sparse crossbars, and show how FPRESSO makes the exploration of a huge search space not only possible but easy, efficient, and affordable, for any class of VTR users.

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Type
doctoral thesis
DOI
10.5075/epfl-thesis-7928
Author(s)
Zgheib, Grace  
Advisors
Ienne, Paolo  
Jury

Prof. Aikaterini Argyraki (présidente) ; Prof. Paolo Ienne (directeur de thèse) ; Prof. Yusuf Leblebici, Prof. Vaughn Betz, Dr Jonathan Greene (rapporteurs)

Date Issued

2017

Publisher

EPFL

Publisher place

Lausanne

Public defense year

2017-08-04

Thesis number

7928

Total of pages

109

Subjects

Field Programmable Gate Array

•

FPGA

•

architecture design

•

architecture modeling

•

transistor design

•

architectural exploration

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FPRESSO

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wireload modeling

•

CAD tools

EPFL units
LAP  
Faculty
IC  
School
IINFCOM  
Doctoral School
EDIC  
Available on Infoscience
August 9, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/139590
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