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Title: SCART: Predicting STT-RAM Cache Retention Times Using Machine Learning
Prior studies have shown that the retention time of the non-volatile spin-transfer torque RAM (STT-RAM) can be relaxed in order to reduce STT-RAM's write energy and latency. However, since different applications may require different retention times, STT-RAM retention times must be critically explored to satisfy various applications' needs. This process can be challenging due to exploration overhead, and exacerbated by the fact that STT-RAM caches are emerging and are not readily available for design time exploration. This paper explores using known and easily obtainable statistics (e.g., SRAM statistics) to predict the appropriate STT-RAM retention times, in order to minimize exploration overhead. We propose an STT-RAM Cache Retention Time (SCART) model, which utilizes machine learning to enable design time or runtime prediction of right-provisioned STT-RAM retention times for latency or energy optimization. Experimental results show that, on average, SCART can reduce the latency and energy by 20.34% and 29.12%, respectively, compared to a homogeneous retention time while reducing the exploration overheads by 52.58% compared to prior work.  more » « less
Award ID(s):
1844952
PAR ID:
10156101
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2019 Tenth International Green and Sustainable Computing Conference (IGSC)
Page Range / eLocation ID:
1 to 7
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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