With increasing clock frequencies and silicon integration, power aware computing has become a critical concern in the design of embedded processors and systems-on-chip. One of the more effective and widely used methods for poweraware computing is dynamic voltage scaling (DVS). In order to obtain the maximum power savings from DVS, it is essen- IntroductionA critical concern for embedded systems is the need to deliver high levels of performance given ever-diminishing power budgets. This is evident in the evolution of the mobile phone: in the last 7 years mobile phones have shown a 50X improvement in talk-time per gram of battery 1 , while at the same time taking on new computational tasks that only recently appeared on desktop computers, such as 3D graphics, audio/video, internet access, and gaming. As the breadth of applications for these devices widens, a single operating point is no longer sufficient to efficiently meet their processing and power consumption requirements. For example, MPEG video playback requires an order-of-magnitude higher performance than playing MP3s. However, running at the performance level necessary for video is energy-inefficient for audio. The gap between high performance and low power can be bridged through the use of dynamic voltage scaling (DVS) [16], where periods of low processor utilization are exploited by lowering the clock frequency to the minimum required level, allowing corresponding reduction in the supply voltage. Since dynamic energy scales quadratically with supply voltage, significant reduction in energy use can be obtained [14].Enabling systems to run at multiple frequency and voltage levels is a challenging process and requires characterization of the processor to ensure that its operation remains correct at the required operating points. The minimum possible supply voltage that results in correct operation is referred to as the critical supply voltage. The critical supply voltage must be sufficient to ensure correct operation in the face of a number of environmental and process related variabilities that can impact circuit performance. These include unexpected voltage drops in the power supply network, temperature fluctuations, gate-length and doping concentration variations, cross-coupling noise, etc. These variabilities may be data dependent, meaning that they exhibit their worst-case impact on circuit performance only under certain instruction and data sequences, and are composed of both local and global components. For instance, local process variations will impact specific regions of the die in different and independent ways, while global process variation impacts the circuit performance of the entire die and creates variation from one die to the next. Similarly, temperature and supply drop have local and global components, while cross-coupling noise is a predominantly local effect.To ensure correct operation under all possible variations, a conservative supply voltage is typically selected at designtime using corner analysis. Hence, margins are added ...

Leakage current has become a stringent constraint in modern processor designs in addition to traditional constraints on frequency. Since leakage current exhibits a strong inverse correlation with circuit delay, effective parametric yield prediction must consider the dependence of leakage current on frequency. In this paper, we present a new chip-level statistical method to estimate the total leakage current in the presence of within-die and die-to-die variability. We develop a closed-form expression for total chip leakage that models the dependence of the leakage current distribution on a number of process parameters. The model is based on the concept of scaling factors to capture the effects of within-die variability. Using this model, we then present an integrated approach to accurately estimate the yield loss when both frequency and power limits are imposed on a design. Our method demonstrates the importance of considering both these limiters in calculating the yield of a lot.

Soft errors have emerged as an important reliability challenge for nanoscale very large scale integration designs. In this paper, we present a fast and efficient soft error rate (SER) analysis methodology for combinational circuits. We first present a novel parametric waveform model based on the Weibull function to represent particle strikes at individual nodes in the circuit. We then describe the construction of the descriptor object that efficiently captures the correlation between the transient waveforms and their associated rate distribution functions. The proposed algorithm consists of operations to inject, propagate, and merge these descriptors while traversing forward along the gates in a circuit. The parameterized waveforms enable an efficient static approach to calculate the SER of a circuit. We exercise the proposed approach on a wide variety of combinational circuits and observe that our algorithm has linear runtime with the size of the circuit. The runtimes for soft error estimation were observed to be in the order of about 1 s, compared to several minutes or even hours for previously proposed methods.

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