A methodology for guided behavioral-level optimization

Abstract

-Optimization at the early stages of design are crucial. However, due to an overwhelming number of design and optimization options, design exploration is often conducted in a qualitative, ad-hoc manner. This paper presents a methodology and interactive environment for guiding the exploration process. A prototype targeting behavioral-level optimization for datapath-intensive ASIC implementations has been developed. The key to the approach is encapsulated knowledge about the various optimizations and a set of techniques to automatically extract the "essence" of a design description. At each stage in the exploration process, the system suggests and ranks potential optimizations, both in terms of immediate and longer-term impact. It also provides evaluations of the design and of the likely affects each optimization will have on metrics like power and performance. In the new approach, the designer is responsible for making the actual optimization selections. However, using the provided guidance, designers can make decisions in a more informed manner, and therefore can explore the design solution space more effectively. The effectiveness of the approach is demonstrated on a number of designs. IntroductionTraditionally, concentration has been placed in the development of point tools and methodologies which address a single task within the optimization process. For example, in behavioral-level optimization, in addition to the approaches proposed for partitioning, template matching, and clock selection, there exist more than a hundred transformations [Bac94,Par95]. Some optimizations are restricted to optimization of a specific class of computation (e.g. only linear computations). Some focus on power optimization while others target area or throughput. Furthermore, some concentrate on reduction of just a specific sub-metric; for example, different power optimization techniques have been developed for reducing capacitance, for reducing activity, and for enabling voltage scaling.Effective global optimization, or optimization of a global objective function on current-day complex and heterogeneous applications, clearly requires the coordinated application of a collection of techniques. While many of the problems being addressed by the individual techniques are in themselves computationally intractable, an added layer of complexity clearly exists in deriving an entire optimization trajectory. This added complexity is due to the strong interdependency that exists between techniques. Exploring different trajectories is crucial, as a large variability in attainable improvements exists. Exploring all possible sequences of actions inevitably results in combinatorial explosion, however, and thus is not a feasible option. Furthermore, static scripts are not sufficient since their effectiveness is strongly dependent on the particular design. This paper proposes a methodology for enabling systematic and effective global optimization. The approach involves interactively and quantitatively guiding the designer's exploration pro...