This paper presents a high-level design methodology, called input space adaptive design, and new design automation algorithms for optimizing energy consumption and performance. Our techniques can be applied to behaviors described in hardware description languages, predesigned register-transfer level (RTL) circuits, or in the context of traditional high-level design methodologies. An input space adaptive design exploits the well-known fact that the quality of circuits can be significantly optimized by employing algorithms and implementation architectures that adapt to input statistics. This paper shows that harnessing the principles of input space adaptive design into a structured high-level design methodology can lead to large improvements in performance and energy consumption. We illustrate the tradeoffs involved in such designs, and demonstrate the need for a systematic design methodology in order to realize the full potential for performance and energy improvements. We propose a methodology for input space adaptive design that consists of the following steps: identification of parts of the behavior that hold the highest potential for optimization, selection of input subspaces whose occurrence can lead to significant reductions in implementation complexity, and transformation of the behavior to realize performance and/or energy savings. Evaluations of performance, energy, and area characteristics of input space adaptive designs in the context of a commercial high-level design flow indicate that such designs can reduce energy consumption by up to 58.9% (average of 40.0%), and simultaneously improve performance by up to 57.5% (average of 41.8%) compared to well-optimized designs that do not employ such techniques. The energy-delay product is reduced by up to 77.9% (average of 64.8%). When the performance improvements are translated into additional energy savings through supply voltage reduction, input space adaptive designs consume up to 74.2% (average of 68.8%) less energy at the same performance. The average area overhead is only 7.4%.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE Transactions on Very Large Scale Integration (VLSI) Systems|
|State||Published - Jun 2004|
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Electrical and Electronic Engineering