Sleep transistor insertion is a key step in low power design methodologies for nanometer CMOS. In the clustered sleep transistor approach, a single sleep transistor is shared among a number of gates and it must be sized according to the maximum current that can be injected onto the virtual ground by the gates in the cluster. A conservative (upper bound) estimate of the maximum injected current is required in order to avoid excessive speed degradation and possible violations of timing constraints. In this paper we propose a scalable algorithm for tightening upper bound computation, with a controlled and tunable computational cost. The algorithm leverages the capabilities of state-of-the-art commercial timing analysis engines, and it is tightly integrated into standard industrial flow for leakage optimization. Benchmark results demonstrate the effectiveness and efficiency of our approach.
Interactive presentation: Efficient computation of discharge current upper bounds for clustered sleep transistor sizing / A. Sathanur; A. Calimera; L. Benini; A. Macii; E. Macii; M. Poncino. - STAMPA. - (2007), pp. 1544-1549. (Intervento presentato al convegno Conference on Design, automation and test in Europe (DATE '07) SESSION: Temperature and process aware low power techniques tenutosi a Nice, France nel 16-20 April 2007).
Interactive presentation: Efficient computation of discharge current upper bounds for clustered sleep transistor sizing
BENINI, LUCA;
2007
Abstract
Sleep transistor insertion is a key step in low power design methodologies for nanometer CMOS. In the clustered sleep transistor approach, a single sleep transistor is shared among a number of gates and it must be sized according to the maximum current that can be injected onto the virtual ground by the gates in the cluster. A conservative (upper bound) estimate of the maximum injected current is required in order to avoid excessive speed degradation and possible violations of timing constraints. In this paper we propose a scalable algorithm for tightening upper bound computation, with a controlled and tunable computational cost. The algorithm leverages the capabilities of state-of-the-art commercial timing analysis engines, and it is tightly integrated into standard industrial flow for leakage optimization. Benchmark results demonstrate the effectiveness and efficiency of our approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.