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Memory designs require timing margins to compensate for aging and fabrication process variations. With technology downscaling, aging mechanisms became more apparent, and larger margins are considered necessary. This, in return, means a larger area requirement and lower performance for the memory. Bias Temperature Instability (BTI) is one of the main contributors to aging, which slows down transistors and ultimately causes permanent faults. In this paper, first, we propose a low-cost aging mitigation scheme, which can be applied to existing hardware to mitigate aging on memory address decoder logic. We mitigate the BTI effect on critical transistors by applying a rejuvenation workload to the memory. Such an auxiliary workload is executed periodically to rejuvenate transistors that are located on critical paths of the address decoder. Second, we analyze workloads' efficiency to optimize the mitigation scheme. Experimental results performed with realistic benchmarks demonstrate several-times lifetime extension with a negligible execution overhead.
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Memory designs require timing margins to compensate for aging and fabrication process variations. With technology downscaling, aging mechanisms became more apparent, and larger margins are considered necessary. This, in return, means a larger area requirement and lower performance for the memory. Bias Temperature Instability (BTI) is one of the main contributors to aging, which slows down transistors and ultimately causes permanent faults. In this paper, first, we propose a low-cost aging mitigation scheme, which can be applied to existing hardware to mitigate aging on memory address decoder logic. We mitigate the BTI effect on critical transistors by applying a rejuvenation workload to the memory. Such an auxiliary workload is executed periodically to rejuvenate transistors that are located on critical paths of the address decoder. Second, we analyze workloads' efficiency to optimize the mitigation scheme. Experimental results performed with realistic benchmarks demonstrate several-times lifetime extension with a negligible execution overhead.
Conference paper(2021)
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F. Ahmed, E. Verschuur, C. Tsingas
Mud-roll comprises of dispersive seismic waves that propagate along the unconsolidated sediment layers at the sea floor in shallow water marine environments, where the water depth is normally less than 30 m. Mud-roll's characteristics are spatially variable, i.e. the dispersion properties change from one shot to another across a seismic survey area. These complex kinematic properties make noise elimination very challenging using conventional seismic processing workflows. Our proposed method is a hybrid, Curvelet transform-based workflow that takes advantage of conventional seismic processing filtering to estimate the noise components, followed by the Curvelet transform that attenuates the residual noise energy that is difficult to remove with a conventional subtraction algorithm. In this paper, we illustrate the proposed Curvelet transform-based workflow using both synthetic and field data and demonstrate its effectiveness.
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Mud-roll comprises of dispersive seismic waves that propagate along the unconsolidated sediment layers at the sea floor in shallow water marine environments, where the water depth is normally less than 30 m. Mud-roll's characteristics are spatially variable, i.e. the dispersion properties change from one shot to another across a seismic survey area. These complex kinematic properties make noise elimination very challenging using conventional seismic processing workflows. Our proposed method is a hybrid, Curvelet transform-based workflow that takes advantage of conventional seismic processing filtering to estimate the noise components, followed by the Curvelet transform that attenuates the residual noise energy that is difficult to remove with a conventional subtraction algorithm. In this paper, we illustrate the proposed Curvelet transform-based workflow using both synthetic and field data and demonstrate its effectiveness.
