Performance models are typically written by hand for a new model or assembled piece-meal from the prior simulation code of an old model. In either case, many man-months of work may be required to write the new model and validate design details against a prior or current design. In reality, the majority of information about the performance of the design already exists in the design structure of either the old hardware model or the new model or both. To harvest this information and eliminate the significant duplicate coding and validation efforts, we propose that a performance model be automatically synthesized from a prior or current hardware design using a bottom-up, design-oriented approach. We demarcate the performance-critical boundaries of the design and perform backward-trace cone analysis to identify logic to include in the performance model. We then abstract specific components for design changes and expend modeling effort only on the few functions relevant to a particular design study. Engineering effort then becomes focused on workload selection and quality, defining and projecting new designs, and assessing design tradeoffs and sensitivities - the small set of tasks with the highest potential to improve design performance.We present a case-study that shows that even the simplest proposed transformations on a high-performance IBM L2 cache design result in a simulation speedup of 3.9, with evidence that an order of magnitude speedup can be obtained using a few additional modeling abstractions.