طراحی تکاملی زیرسیستم حافظه Evolutionary design of the memory subsystem

1. Introduction Memory hierarchy has a significant impact on performance and energy consumption in the system. This impact is estimated about 50% of the total energy consumption in the chip [1]. This places the memory subsystem as one of the most important sources to improve both performance and energy consumption. Concerns such as thermal issues or high energy consumption can cause a significant performance degradation, as well as irreversible damages to the devices therefore increasing the energy cost. Previous works have shown that saving energy in the memory subsystem can effectively control transistors aging effect and can significantly extend lifetime of the internal structures [2]. Technological changes combined with the development of communications have led to the great expansion of mobile devices such as smartphones, tablets, etc. Mobile devices have evolved rapidly to adapt to the new requirements, giving support to multimedia applications. These devices are supplied with embedded systems, which are mainly battery-powered and usually have less computing resources than desktop systems. Additionally, multimedia applications are usually memory intensive, so they have high performance requirements which implies a high energy consumption. These features increase the pressure on the whole memory subsystem. Processor registers, smaller in size, work at the same speed than the processor and consume less energy compared with other levels of the memory subsystem. However, the energy consumption and access time rise when the file size increases due to a higher number of registers and ports. Regarding the cache memory, it has been identified as a cold area in the chip, although the peripheral circuits and the size of the cache are the most influencing factors to cause a temperature increase [3], facing the accesses to the cache memory because of specific applications. However, cache memory affects both performance and energy consumption. In fact, energy consumption of the on chip cache memory is considered to be responsible of 20–30% of the total consumption in the chip [4]. A suitable cache configuration will improve both metrics.