Due to low power requirements of many embedded/portable devices such as mobile phones and laptop computers and dramatic increases in clock frequencies of general-purpose processors, lowpower software technology is becoming increasingly important in system design. Many applications from image and video processing as well as from dense linear algebra are array-dominated and data-intensive, thereby spending a major portion of their execution time and energy in the memory subsystem. This paper presents a compiler-based optimization framework that targets reducing the energy consumption in a partitioned off-chip memory architecture that contains multiple memory banks by organizing the order of computations and the layout of data. The optimizations considered in this work take advantage of low-power operating modes and the partitioned (multi-bank) structure of the off-chip memory. Our preliminary experiments show that the proposed framework improves memory energy by up to 86% over a scheme that keeps all the memory banks in the active (fully-operational) operating mode all the time, and up to 70% over a scheme that utilizes low-power operating modes without doing any loop and data optimizations.
The problem of minimizing the energy consumption of the memory subsystem is dealt with in REF .
Energy-oriented compiler optimizations for partitioned memory architectures
CASES '00
Computer Science