Automatic nonlinear memory power modelling
Proceedings of the conference on Design, automation and test in Europe
From architecture to layout: partitioned memory synthesis for embedded systems-on-chip
Proceedings of the 38th annual Design Automation Conference
Layout-driven memory synthesis for embedded systems-on-chip
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Memory power models for multilevel power estimation and optimization
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Architectural leakage-aware management of partitioned scratchpad memories
Proceedings of the conference on Design, automation and test in Europe
An Efficient Approach with Scaling Capability to Improve Existing Memory Power Model
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A scalable power modeling approach for embedded memory using LIB format
PATMOS'06 Proceedings of the 16th international conference on Integrated Circuit and System Design: power and Timing Modeling, Optimization and Simulation
A hybrid and adaptive model for predicting register file and SRAM power using a reference design
Proceedings of the 49th Annual Design Automation Conference
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We present our methodology for developing models of on-chip SRAM memory organizations. The models were created to enable the quick evaluation of energy, area, and performance of different memory configurations considered during synthesis. The models are defined in terms of parameters, such as size and mode of operation, which are known at synthesis time. Our methodology does not require knowledge of the underlying memory circuitry and provides models with average percentage errors within 8%. We examine the importance of the different parameters in the models to reduce the time required to develop the models. We found that only ten different memories from a large span of possible memory sizes are needed to obtain reasonably accurate models, with average errors within 15%. In this paper, we present our modeling methodology, discuss the important aspects in developing the models, and examine the parameters necessary in creating accurate models quickly and easily.