Machine learning techniques for taming the complexity of modern hardware design

The continual quest to improve performance and efficiency for new generations of IBM servers leads to a corresponding increase in system complexity. As hardware complexity increases, i.e., more complicated hardware architectures requiring more design choices, the level of sophistication in automation also increases to manage the design challenges. The number of design choices in modern hardware design calls for intelligent automated techniques to navigate the design space. This paper covers three machine learning-based automation techniques used during the design and lifetime of IBM systems. In particular, we describe applying these techniques to the IBM z13 mainframe. During the presilicon design phase, a software system called synthesis tuning system is employed to optimize the parameters of the synthesis program vital to hardware implementation. During both the presilicon and postsilicon phases of the design, a framework called MicroProbe automatically generates microbenchmarks, i.e., small programs, to determine power, performance, and resilience characteristics of the system. Following system product deployment in customer environments, the Call Home facility monitors and analyzes a wide range of in-field usage metrics to help administrators understand current system behavior and improve future designs. Beyond existing IBM system contributions, this high-level overview paper also describes additional machine learning (and related) techniques in the field of hardware design, along with future directions for such work.