Automating technology adaptation in design synthesis

The goal of design synthesis is the generation of high-quality material designs from abstract specifications. Recent efforts in VLSI design synthesis (logic synthesis) have shown how heuristic techniques can generate human-quality hardware designs in a fraction of manual design time. Despite successes, the state of the art in logic synthesis is limited in terms of range and quality. Existing logic synthesis tools are largely restricted to designing combinational circuits described by Boolean equations. Such circuits compose only about 20 percent of a high-level design; the other 80 percent consists of complex components described in terms of their functionality, such as arithmetic and logic units, counters, and processors. Existing tools are also restricted to implementing designs with a small set of widely available physical cells, such as one- and two-level Boolean gates. While this restriction makes a synthesis tool independent of a particular component library, it also keeps the tool from using complex but nonstandard library components that might otherwise improve design quality.To increase the capabilities and quality of synthesis tools, we propose to develop a new knowledge-intensive model of design synthesis that we call the derivational-process model. This model uses knowledge of design styles to decompose component specifications and generate designs that are appropriate to constraints. To keep this model robust to technology changes, we further propose to develop an adjunct learning component called the model of technology adaptation. This model uses knowledge of fundamental principles of design to acquire design rules that take advantage of complex library components or that reflect new design styles. The combination of these two models we refer to as adaptive design synthesis. This work contributes to recent work in Logic Synthesis, Knowledge-Based Design, and Machine Learning