Feedback Learning Based Dead Write Termination for Energy Efficient STT-RAM Caches

Abstract

Spin-torque transfer RAM (STT-RAM) is a promising candidate to replace SRAM for larger Last level cache (LLC). However, it has long write latency and high write energy which diminish the benefit of adopting STT-RAM caches. A common observation for LLC is that a large number of cache blocks have never been referenced again before they are evicted. The write operations for these blocks, which we call dead writes, can be eliminated without incurring subsequent cache misses. To address this issue, a quantitative scheme called Feedback learning based dead write termination (FLDWT) is proposed to improve energy efficiency and performance of STT-RAM based LLC. FLDWT dynamically learns the block access behavior by using data reuse distance and data access frequency, and then classifies the blocks into dead blocks and live blocks. FLDWT terminates dead write block requests and improves the estimation accuracy via feedback information. Compared with STT-RAM baseline in the lastlevel caches, experimental results show that our scheme achieves energy reduction by 44.6% and performance improvement by 12% on average with negligible overhead.