The myriad engines that make up the Synopsys RTL-to-signoff design flow operate as a constant interplay–a waltz-like dance, if you will–of golden signoff-enabled analysis (including timing, power, area, IR-drop, and DRCs) alongside targeted, highly tuned optimizations. Their singular goal is to co-enable the achievement of power, performance, and area (PPA) targets as smoothly and efficiently as possible. This constant analysis generates vast volumes of data, much of which is highly unstructured. However, this mass of disconnected data points, when taken together, can provide a detailed understanding of the “health” of the design. For example, where are additional opportunities for design improvement? What can be addressed earlier in the flow to bring this design to closure?
Log files do go some way to expose this data. Still, with the size and complexity of the current crop of designs, only small views into the vast underlying data are possible before they become too unwieldy to comprehend. Engineers, therefore, have long had to try to second-guess what data may be helpful to them and then use proactive data-dumping from the tools to extract what they hope is the necessary information. And then, they might apply multi-tool parsing of traditional data sources, like the log files, to combine and piece together what they eventually need to drive debugging. What has always been missing is a way to amalgamate and connect this data–the deep engine metrics and their associated analysis–into a grand, holistic view. Having a means to, for example, efficiently “connect the dots” between a timing issue in one part of the design and a congestion issue somewhere else would be highly beneficial.
The Synopsys Design.da solution presents such a holistic view of all project data and then drives it many steps further (Figure 1). The solution efficiently and autonomously siphons metrics data while also intelligently curating the associated analysis data directly from Synopsys’ unique single data model. Then, it transforms and loads the data into always-on, industry-standard databases. Handling flow metrics from a third-party tool is equally simple. With the data available in tool-decoupled databases, searching, filtering, graphing, comparing, and trending are simple, intuitive tasks. As more runs progress, that data–indeed, the data from the entire project team–can also be absorbed, compared, and cross-referenced through a responsive, web-based user interface (UI) and then shared with ease.