Abstract

For the sake of higher cell density while achieving near-zero standby power, recent research progress in Magnetic Tunneling Junction (MTJ) devices has leveraged Multi-Level Cell (MLC) configurations of Spin-Transfer Torque Random Access Memory (STT-RAM). However, in order to mitigate the write disturbance in an MLC strategy, data stored in the soft bit must be restored back immediately after the hard bit switching is completed. Furthermore, as the result of MTJ feature size scaling, the soft bit can be expected to become disturbed by the read sensing current, thus requiring an immediate restore operation to ensure the data reliability. In this paper, we design and analyze a novel Adaptive Restore Scheme for Write Disturbance (ARS-WD) and Read Disturbance (ARS-RD), respectively. ARS-WD alleviates restoration overhead by intentionally overwriting soft bit lines which are less likely to be read. ARS-RD, on the other hand, aggregates the potential writes and restore the soft bit line at the time of its eviction from higher level cache. Both of these two schemes are based on a lightweight forecasting approach for the future read behavior of the cache block. Our experimental results show substantial reduction in soft bit line restore operations. Moreover, ARS promotes advantages of MLC to provide a preferable L2 design alternative in terms of energy, area and latency product compared to SLC STT-RAM alternatives. Whereas the popular Cell Split Mapping (CSM) for MLC STT-RAM leverages the inter-block nonuniform access frequency, the intra-block data access features remain untapped in the MLC design. Aiming to minimize the energy-hungry write request to Hard-Bit Line (HBL) and maximize the dynamic range in the advantageous Soft-Bit Line (SBL), an hybrid mapping strategy for MLC STT-RAM cache (Double-S) is advocated in the paper. Double-S couples the contemporary Cell-Split-Mapping with the novel Word-Split-Mapping (WSM). Sparse cache block detector and read depth based data allocation/ migration policy are proposed to release the full potential of Double-S.

Notes

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Graduation Date

2017

Semester

Fall

Advisor

Wang, Jun

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computer Engineering

Degree Program

Computer Engineering

Format

application/pdf

Identifier

CFE0006865

URL

http://purl.fcla.edu/fcla/etd/CFE0006865

Language

English

Release Date

December 2022

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Open Access)

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