Behavioral network graph unifying the domains of high-level and logic synthesis

Abstract

High-level synthesis operates on internal models known as control/data flow graphs (CDFG) and produces a register-transfer-level (RTL) model of the hardware implementation for a given schedule. For high-level synthesis to be efficient it has to estimate the effect that a given algorithmic decision (e.g., scheduling, allocation) will have on the final hardware implementation (after logic synthesis). Currently, this effect cannot be measured accurately because the CD-FGs are very distinct from the RTL/gate-level models used by logic synthesis, precluding interaction between high-level and logic synthesis. This paper presents a solution to this problem consisting of a novel internal model for synthesis which spans the domains of high-level and logic synthesis. This model is an RTL/gate-level network capable of representing all possible schedules that a given behavior may assume. This representation allows high-level synthesis algorithms to be formulated as logic transformations and effectively interleaved with logic synthesis.