A heterogeneous memory organization with minimum energy consumption in 3D chip-multiprocessors
Date
2016-05Source Title
IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) 2016
Publisher
IEEE
Language
English
Type
Conference PaperItem Usage Stats
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Abstract
Main memories play an important role in overall energy consumption of embedded systems. Using conventional memory technologies in future designs in nanoscale era cause 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 propose a stacked hybrid memory system for 3D chip-multiprocessors to take advantages of both traditional and non-volatile memory technologies. For reaching this target, we present a convex optimization-based model that minimizes the system energy consumption while satisfy endurance constraint in order to design a reliable memory system. Experimental results show that the proposed method improves energy-delay product (EDP) and performance by about 44.8% and 13.8% on average respectively compared with the traditional memory design where single technology is used. © 2016 IEEE.
Keywords
Convex-optimization problemDark silicon
Embedded Chip-Multiprocessor (eCMP)
Heterogeneous memory system
Non-Volatile Memory (NVM)
Adaptive systems
Convex optimization
Digital storage
Embedded systems
Energy utilization
Multiprocessing systems
Nonvolatile storage
Optimization
Product design
Convex optimization problems
Dark silicons
Embedded chips
Heterogeneous memory
Non-volatile memory
Data storage equipment
Permalink
http://hdl.handle.net/11693/37481Published Version (Please cite this version)
http://dx.doi.org/10.1109/CCECE.2016.7726817Collections
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