Parameter variations reveal themselves as different frequency and leakage powers per instances of the same MPSoC. By the increasing variation with technology scaling, worst-case-based scheduling algorithms result in either increasingly less optimal schedules or otherwise more lost yield. To address this problem, this paper introduces a variation-aware task and communication scheduling algorithm for multiprocessor system-on-chip (MPSoC). We consider both delay and leakage power variations during the process of finding the best schedule so that leakier processors are less utilized and can be more frequently put in sleep mode to reduce power. Our algorithm takes advantage of event tables to accelerate the statistical timing and power analysis. We use genetic algorithm to find the best schedule that maximizes power-yield under a performance-yield constraint. Experimental results on real world benchmarks show that our proposed algorithm achieves 16.6% power-yield improvement on average over deterministic worst-case-based scheduling.

The share of leakage in cache power consumption increases with technology scaling. Choosing a higher threshold voltage (Vth) and/or gate-oxide thickness (Tox) for cache transistors improves leakage, but impacts cell delay. We show that due to uncorrelated random within-die delay variation, only some (not all) of cells actually violate the cache delay after the above change. We propose to add a spare cache way to replace delay-violating cache-lines separately in each cache-set. By SPICE and gate-level simulations in a commercial 90 nm process, we show that choosing higher Vth, Tox and adding one spare way to a 4-way 16 KB cache reduces leakage power by 42%, which depending on the share of leakage in total cache power, gives up to 22.59% and 41.37% reduction of total energy respectively in L1 instruction- and L2 unified-cache with a negligible delay penalty, but without sacrificing cache capacity or timing-yield.

Energy consumption is a major concern in embedded computing systems. Several studies have shown that cache memories account for 40% or more of the total energy consumed in these systems. Active power used to be the primary contributor to total power dissipation of CMOS designs, but with the technology scaling, the share of leakage in total power consumption of digital systems continues to grow. Moreover, temperature is another factor that exponentially increases the leakage current. In this paper, we show the effect of temperature on the optimal (minimum-energy-consuming) cache configuration for low energy embedded systems. Our results show that for a given application and technology, the optimal cache size moves toward smaller caches at higher temperatures, due to the larger leakage. Consequently, a Temperature-Aware Configurable Cache (TACC) is an effective way to save energy in finer technologies when the embedded system is used in different temperatures. Our results show that using a TACC, up to 61% energy can be saved for instruction cache and 77% for data cache compared to a configurable cache that has been configured for only the corner-case temperature (100). Furthermore, the TACC also enhances the performance by up to 28% for the instruction cache and up to 17% for the data cache.