An evolutionary approach to behavioural-level synthesis

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 hierarchical genetic algorithm (HGA). We focus on the core problems of HLS: scheduling, allocation, and binding. Scheduling consists of assigning of operations in an 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 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 functions for the first. When compared to other, well-known techniques, our results show a reduction in time to obtain optimal solutions for standard benchmarks.