An evolutionary approach to behavioural-level synthesis

ABSRACT This paper presents a novel approach to the concurrent solution of three High-Level Synthesis (HLS) problems and solves them in an integrated manner using a Hierarchical Genetic Algorithm (HGA). We focus on the core problems of HLS: Scheduling, Allocation, and Binding. Scheduling consists of assigning of operations in a Data-Flow Graph (DFG) to control steps or clock cycles. Allocation selects specific numbers and types of functional units from a hardware library to perform the operations specified in the DFG. Binding assigns constituent operations of the DFG to specific unit instances. A very general version of the problem is considered where functional units may perform different operations in different numbers of control steps. The HLS problems are solved by applying two genetic algorithms in a hierarchical manner. The first performs allocation, while the second performs scheduling and binding and serves as the fitness function for the first. When compared to other, well-known techniques, our results show a reduction in time to obtain optimal solutions for standard benchmarks.