Hardware/software partitioning and pipelined scheduling on runtime reconfigurable FPGAs

Authors:
Mingxuan Yuan;Zonghua Gu;Xiuqiang He;Xue Liu;Lei Jiang
Affiliations:
Zhejiang University, P. R. China and Hong Kong University of Science and Technology;Zhejiang University, P. R. China and Hong Kong University of Science and Technology;Zhejiang University, P. R. China and Hong Kong University of Science and Technology;Mcgill University, Quebec, Canada;University of Pittsburgh, Pittsburgh, PA
Venue:
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Year:
2010

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Abstract

FPGAs are widely used in today's embedded systems design due to their low cost, high performance, and reconfigurability. Partially RunTime-Reconfigurable (PRTR) FPGAs, such as Virtex-2 Pro and Virtex-4 from Xilinx, allow part of the FPGA area to be reconfigured while the remainder continues to operate without interruption, so that HW tasks can be placed and removed dynamically at runtime. We address two problems related to HW task scheduling on PRTR FPGAs: (1) HW/SW partitioning. Given an application in the form of a task graph with known execution times on the HW (FPGA) and SW (CPU), and known area sizes on the FPGA, find an valid allocation of tasks to either HW or SW and a static schedule with the optimization objective of minimizing the total schedule length (makespan). (2) Pipelined scheduling. Given an input task graph, construct a pipelined schedule on a PRTR FPGA with the goal of maximizing system throughput while meeting a given end-to-end deadline. Both problems are NP-hard. Satisfiability Modulo Theories (SMT) is an extension to SAT by adding the ability to handle arithmetic and other decidable theories. We use the SMT solver Yices with Linear Integer Arithmetic (LIA) theory as the optimization engine for solving the two scheduling problems. In addition, we present an efficient heuristic algorithm based on kernel recognition for the pipelined scheduling problem, a technique borrowed from SW pipelining, to overcome the scalability problem of the SMT-based optimal solution technique.