Notice of violation of IEEE publication principles an energy-efficient heterogeneous memory architecture for future dark silicon embedded chip-multiprocessors
IEEE Transactions on Emerging Topics in Computing
IEEE Computer Society
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Main memories play an important role in overall energy consumption of embedded systems. Using conventional memory technologies in future designs in nanoscale era causes a drastic increase in leakage power consumption and temperature-related problems. Emerging non-volatile memory (NVM) technologies offer many desirable characteristics such as near-zero leakage power, high density and non-volatility. They can significantly mitigate the issue of memory leakage power in future embedded chip-multiprocessor (eCMP) systems. However, they suffer from challenges such as limited write endurance and high write energy consumption which restrict them for adoption in modern memory systems. In this article, we present a convex optimization model to design a 3D stacked hybrid memory architecture in order to minimize the future embedded systems energy consumption in the dark silicon era. This proposed approach satisfies endurance constraint in order to design a reliable memory system. Our convex model optimizes numbers and placement of eDRAM and STT-RAM memory banks on the memory layer to exploit the advantages of both technologies in future eCMPs. Energy consumption, the main challenge in the dark silicon era, is represented as a major target in this work and it is minimized by the detailed optimization model in order to design a dark silicon aware 3D Chip-Multiprocessor. Experimental results show that in comparison with the Baseline memory design, the proposed architecture improves the energy consumption and performance of the 3D CMP on average about 61.33% and 9% respectively. IEEE
Keywords3D integration technology
Energy efficient design
Heterogeneous memory architecture
Non-Volatile Memory (NVM)
Published Version (Please cite this version)https://doi.org/10.1109/TETC.2016.2563323
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Ozturk, O.; Kandemir, M.; Irwin, M. J. (Institute of Electrical and Electronics Engineers, 2009-06)The memory system presents one of the critical challenges in embedded system design and optimization. This is mainly due to the ever-increasing code complexity of embedded applications and the exponential increase seen in ...
Onsori, Salman; Asad, Arghavan; Raahemifar, K.; Fathy, M. (IEEE, 2015-11)In this article, we present a convex optimization model to design a stacked hybrid memory system for 3D embedded chip-multiprocessors (eCMP). Our convex model optimizes numbers and placement of SRAM and STT-RAM memories ...
Diouf, B.; Hantaş, C.; Cohen, A.; Özturk, Ö.; Palsberg, J. (Association for Computing Machinery, 2013)Compilers use software-controlled local memories to provide fast, predictable, and power-efficient access to critical data. We show that the local memory allocation for straight-line, or linearized programs is equivalent ...