2004 · 842 citations · 18 references
EngineeringComputer EngineeringElectrical EngineeringLow-power DesignMicroelectronicsSystem-on-chip DesignEnergy-efficient Embedded SystemsLow-power Digital Circuit DesignPower-aware DesignTiming AnalysisVoltage ScalingDynamic ScalingDynamic Voltage And Frequency ScalingFlip-flop DesignLow-power Flip-flop DesignSupply Voltage OptimizationTiming Error CorrectionTiming Error DetectionVoltage Scaling In Digital Circuits
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 power-aware computing is dynamic voltage scaling (DVS). In order to obtain the maximum power savings from DVS, it is essential to scale the supply voltage as low as possible while ensuring correct operation of the processor. The critical voltage is chosen such that under a worst-case scenario of process and environmental variations, the processor always operates correctly. However, this approach leads to a very conservative supply voltage since such a worst-case combination of different variabilities is very rare. In this paper, we propose a new approach to DVS, called Razor, based on dynamic detection and correction of circuit timing errors. The key idea of Razor is to tune the supply voltage by monitoring the error rate during circuit operation, thereby eliminating the need for voltage margins and exploiting the data dependence of circuit delay. A Razor flip-flop is introduced that double-samples pipeline stage values, once with a fast clock and again with a time-borrowing delayed clock. A metastability-tolerant comparator then validates latch values sampled with the fast clock. In the event of timing error, a modified pipeline mispeculation recovery mechanism restores correct program state. A prototype Razor pipeline was designed in a 0.18 /spl mu/m technology and was analyzed. Razor energy overhead during normal operation is limited to 3.1%. Analyses of a full-custom multiplier and a SPICE-level Kogge-Stone adder model reveal that substantial energy savings are possible for these devices (up to 64.2%) with little impact on performance due to error recovery (less than 3%).
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Automatically characterizing large scale program behavior
Timothy Sherwood, Erez Perelman, Greg Hamerly et al. · 2002 · 1.7K citations
SimpleScalar: an infrastructure for computer system modeling
T. Austin, Eric D. Larson, Daniel Ernst · Computer · 2002 · 1.6K citations
Ramachendra P. Batni · Journal of the Franklin Institute · 1972 · 1.1K citations