A survey of system level power management schemes in the dark-silicon era for many-core architectures

Power consumption in Complementary Metal Oxide Semiconductor (CMOS) technology has escalated to a point that only a fractional part of many-core chips can be powered-on at a time. Fortunately, this fraction can be increased at the expense of performance through the dark-silicon solution. However, with many-core integration set to be heading towards its thousands, power consumption and temperature increases per time, meaning the number of active nodes must be reduced drastically. Therefore, optimized techniques are demanded for continuous advancement in technology. Existing efforts try to overcome this challenge by activating nodes from different parts of the chip at the expense of communication latency. Other efforts on the other hand employ run-time power management techniques to manage the power performance of the cores trading-off performance for power. We found out that, for a significant amount of power to saved and high temperature to be avoided, focus should be on reducing the power consumption of all the on-chip components. Especially, the memory hierarchy and the interconnect. Power consumption can be minimized by, reducing the size of high leakage power dissipating elements, turning-off idle resources and integrating power saving materials

Reducing set-associative L1 data cache energy by early load data dependence detection (ELD3)

Fast set-associative level-one data caches (L1 DCs) access all ways in parallel during load operations for reduced access latency. This is required in order to resolve data dependencies as early as possible in the pipeline, which otherwise would suffer from stall cycles. A significant amount of energy is wasted due to this fast access, since the data can only reside in one of the ways. While it is possible to reduce L1 DC energy usage by accessing the tag and data memories sequentially, hence activating only one data way on a tag match, this approach significantly increases execution time due to an increased number of stall cycles. We propose an early load data dependency detection (ELD 3) technique for in-order pipelines. This technique makes it possible to detect if a load instruction has a data dependency with a subsequent instruction. If there is no such dependency, then the tag and data accesses for the load are sequentially performed so that only the data way in which the data resides is accessed. If there is a dependency, then the tag and data arrays are accessed in parallel to avoid introducing additional stall cycles. For the MiBench benchmark suite, the ELD3 technique enables about 49% of all load operations to access the L1 DC sequentially. Based on 65-nm data using commercial SRAM blocks, the proposed technique reduces L1 DC energy by 13%