Modern embedded processors commonly use a set-associative scheme to reduce cache misses. However, a conventional set-associative cache has its drawbacks in terms of power consumption because it has to probe all ways to reduce the access time, although only the matched way is used. The energy spent in accessing the other ways is wasted, and the percentage of such energy will increase as cache associativity increases. Previous research, such as phased caches, way prediction caches and partial tag comparison, have been proposed to reduce the power con- sumption of set-associative caches by optimizing the cache access mode. However, these methods are not adaptable according to the program behavior because of using a single access mode throughout the program execution. In this paper, we propose a behavior-based adaptive access-mode for set-associative caches in embedded systems, which can dynamically adjust the access modes during the program execution. First, a program is divided into several phases based on the principle of program behavior repetition. Then, an off-system pre-analysis is used to exploit the optimal access mode for each phase so that each phase employs the different optimal access mode to meet the application's demand during the program execution. Our proposed approach requires little hardware overhead and commits most workload to the software, so it is very effective for embedded processors. Simulation by using Spec 2000 shows that our proposed approach can reduce roughly 76.95% and 64.67% of power for an instruction cache and a data cache, respectively. At the same time, the performance degradation is less than 1%.
ASJC Scopus subject areas
- Computer Science(all)