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Quantitative Optimization and Early Cost Estimation of Low-Power Hierarchical-Architecture SRAMs Based on Accurate Cost Models

Abstract : Dedicated low-power SRAMs are frequently used in various system-on-chip designs and their power consumption plays an increasingly crucial role in the overall power budget. However, the broad amount of choices regarding the capacity, wordlengths and operational modes make it hard for designers to determine the optimal SRAM architecture. Additionally, many low-power techniques and circuits are frequently utilized but not supported by previously proposed cost models. In order to solve these problems, a cost-model based quantitative optimization approach is proposed. In particular, a fast and accurate power estimation model is built for aiding the low-power SRAM designs. It precisely fits the various complex SRAM circuits and architectures. The quantitative approach provides useful conclusions early in the design phase guiding further optimizations. The estimation error of the power model has been proven to be less than 10 % compared to results based on time-hungry extracted-netlist simulations in a 40-nm CMOS technology.
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Yuan Ren, Tobias Noll. Quantitative Optimization and Early Cost Estimation of Low-Power Hierarchical-Architecture SRAMs Based on Accurate Cost Models. 21th IFIP/IEEE International Conference on Very Large Scale Integration - System on a Chip (VLSI-SoC), Oct 2013, Istanbul, Turkey. pp.69-93, ⟨10.1007/978-3-319-23799-2_4⟩. ⟨hal-01380299⟩

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