Behavioral-Level Guidance Using Property-Based Design Lisa Marie Guerra Doctor of Philosophy in Engineering --- Electrical Engineering and Computer Sciences University of California at Berkeley Professor Jan M. Rabaey, Chair The growing importance of optimization, short time to market windows, and exponentially growing design complexity are just a few of the factors shaping the state-of-the-art synthesis process. In particular, optimization at the early stages of design is crucial --- at the system and behavioral levels, orders of magnitude performance improvement in key design metrics such as throughput, power, and area can be attained. This requires, however, strategic and coordinated application of design techniques best suited for a target design. The problem, however, is the number of options currently available is overwhelming, and as a result, design exploration is often conducted in a qualitative, ad-hoc manner.

Reduction of the number of operations optimizes the important design metrics such as area, cost, throughput, and power consumption for both custom ASIC and programmable processor implementations. We propose a novel technique to minimize the number of operations in DSP computations. The first step of the approach logically partitions a computation into strongly connected components. The second step optimizes each component separately. In the third step the components are merged to further optimize. Finally, the components are scheduled to minimize memory consumption. The effectiveness of our approach is demonstrated on real-life examples.

We optimize implementations of one-dimensional and multidimensional signal processing algorithms by rewriting subexpressions according to a set of algebraic identities. We encode the algebraic identities as conditional rules, and program hill climbing and simulated annealing search techniques to apply the rules. Both of these search techniques avoid an exponential explosion in memory usage because they only keep a single state in memory instead of building the entire tree of possible equivalent forms. We compare the effectiveness of these search techniques in optimizing implementations of onedimensional multirate signal processing algorithms. Our prototype environment is written in Mathematica.