Combined word-length optimization and high-level synthesis of digital signal processing systems

Abstract

Conventional approaches for fixed-point implementation of digital signal processing algorithms require the scaling and word-length (WL) optimization at the algorithm level and the high-level synthesis for functional unit sharing at the architecture level. However, the algorithm-level WL optimization has a few limitations because it can neither utilize the functional unit sharing information for signal grouping nor estimate the hardware cost for each operation accurately. In this study, we develop a combined WL optimization and high-level synthesis algorithm not only to minimize the hardware implementation cost, but also to reduce the optimization time significantly. This software initially finds the WL sensitivity or minimum WL of each signal throughout fixed-point simulations of a signal flow graph, performs the WL conscious high-level synthesis where signals having the similar WL sensitivity are assigned to the same functional unit, and then conducts the final WL optimization by iteratively modifying the WLs of the synthesized hardware model. A list-scheduling-based and an integer linear-programming-based algorithms are developed for the WL conscious high-level synthesis. The hardware cost function to minimize is generated by using a synthesized hardware model. Since fixed-point simulation is used to measure the performance, this method can be applied to general, including nonlinear and time-varying, digital signal processing systems. A fourth-order infinite-impulse response filter, a fifth-order elliptic filter, and a 12th-order adaptive least mean square filter are implemented using this software.