Behavior-level observability don't-cares and application to low-power behavioral synthesis

摘要

Many techniques for power management employed in advanced RTL synthesis tools rely explicitly or implicitly on observability don't-care (ODC) conditions. In this paper we present a systematic approach to maximizing the effectiveness of these techniques by generating power-friendly RTL descriptions in a behavioral synthesis tool. We first introduce the concept of behavior-level observability and investigate its relation with observability under a given schedule, using an extension of Boolean algebra. We then propose an efficient algorithm to compute behavior-level observability on a data-flow graph. Our algorithm exploits knowledge about select and Boolean instructions, and allows certain forms of other knowledge, once uncovered, to be considered for stronger observability conditions. We also describe a behavioral synthesis flow where behavior-level observability is used to guide the scheduler toward maximizing the likelihood that execution of power-hungry instructions will be avoided under a latency constraint. Experimental results show that our approach is able to reduce total power, and it outperforms a previous method in [15] by 17.7% on average, on a set of real-world designs. To the best of our knowledge, this is the first work to use a comprehensive behavioral-level observability analysis to guide optimizations in behavioral synthesis.