cfaed Publications

@Article{hameed_tvlsi18,
author = {Fazal Hameed and Asif Ali Khan and Jeronimo Castrillon},
title = {Performance and Energy Efficient Design of STT-RAM Last-Level-Cache},
journal = {IEEE Transactions on Very Large Scale Integration Systems (TVLSI)},
year = {2018},
volume = {26},
number = {6},
pages = {1059--1072},
month = jun,
abstract = {Recent research has proposed having a die-stacked last-level cache (LLC) to overcome the memory wall. Lately, spin-transfer-torque random access memory (STT-RAM) caches have received attention, since they provide improved energy efficiency compared with DRAM caches. However, recently proposed STT-RAM cache architectures unnecessarily dissipate energy by fetching unneeded cache lines (CLs) into the row buffer (RB). In this paper, we propose a selective read policy for the STT-RAM which fetches those CLs into the RB that are likely to be reused. In addition, we propose a tags-update policy that reduces the number of STT-RAM writebacks. This reduces the number of reads/writes and thereby decreases the energy consumption. To reduce the latency penalty of our selective read policy, we propose the following performance optimizations: 1) an RB tags-bypass policy that reduces STT-RAM access latency; 2) an LLC data cache that stores the CLs that are likely to be used in the near future; 3) an address organization scheme that simultaneously reduces LLC access latency and miss rate; and 4) a tags-to-column mapping policy that improves access parallelism. For evaluation, we implement our proposed architecture in the Zesto simulator and run different combinations of SPEC2006 benchmarks on an eight-core system. We compare our approach with a recently proposed STT-RAM LLC with subarray parallelism support and show that our synergistic policies reduce the average LLC dynamic energy consumption by 75\% and improve the system performance by 6.5\%. Compared with the state-of-the-art DRAM LLC with subarray parallelism, our architecture reduces the LLC dynamic energy consumption by 82\% and improves system performance by 6.8\%.},
doi = {10.1109/TVLSI.2018.2804938},
file = {:/Users/jeronimocastrillon/Documents/Academic/mypapers/1803_Hameed_TVLSI.pdf:PDF},
issn = {1063-8210},
numpages = {14},
url = {http://ieeexplore.ieee.org/document/8307465/}
}